Pith. sign in

REVIEW 2 major objections 4 minor 48 references

Interactive Information Need Prediction with Intent and Context

T0 review · 2 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A user-selected context span plus a short optional intent phrase gives generation and retrieval models enough to predict the user's full information need, and the intent phrase offsets the distraction of larger contexts.

desk verdict New interactive framing for information need prediction, but the 'intent helps' result may be an artifact of extracting intents from the target questions; needs an independent control. read the letter →

arxiv 2501.02635 v1 pith:YOG6UURQ submitted 2025-01-05 cs.IR

Kevin Ros , Dhyey Pandya , ChengXiang Zhai This is my paper · ORCID
classification cs.IR
keywords informationneedpredictionpre-searchcontextsearchintentinteractiveretrievalquestiongenerationpassageconversationalqueryformulation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper studies whether a user's information need can be predicted before it is typed as a query. The interaction it proposes is lightweight: the user highlights a span of what they are reading (from a single word to a whole paragraph) and, optionally, types a few words about what they want to know about that span, such as 'how' or 'hatch time'. The system then either generates the full natural-language question or retrieves the passage that would answer it. Across two adapted datasets and both generation and retrieval models, the paper finds that larger selected contexts degrade prediction, while adding even a short partial intent reliably recovers much of the lost performance. The authors conclude that this interaction strategy is suitable for real-world search systems.

What carries the argument

The central object is the (pre-search context, partial search intent) input pair. The context is a user-selected span of a source text, ranging from a single word to a full paragraph, and the intent is an optional short phrase indicating what the user wants to know about that span. The paper operationalizes this pair as five input conditions and measures how well question-generation and retrieval models recover the full need from each. The comparisons that carry the argument are RQ1, which contrasts the selected context with the full source, and RQ2, which contrasts inputs with and without the intent phrase. The authors' mechanism, grounded in the qualitative examples, is that a larger context introduces competing topics, while the intent phrase suppresses that distraction and guides the model toward the intended target.

What would settle it

A user study where participants highlight text and then type their own intent words, without having seen the target question, would settle the claim: if the retrieval and generation gains over the no-intent condition shrink or disappear when intents are genuinely user-supplied, the paper's conclusion that partial intent mitigates large contexts would be falsified.

Watch

Extended reading notes

Core claim

The paper's central claim is that information need prediction can be cast as an interactive task in which the user supplies a selected pre-search context and an optional partial search intent, and that current generation and retrieval models can solve the task well enough to be useful. The claim is supported by controlled comparisons across five input conditions: the full question, the context plus intent, the source plus intent, the context alone, and the source alone. In the question-generation experiments, fine-tuned Flan-T5 and prompted Llama-3-8B-Instruct and Yi-34B-Chat produce higher BLEU and ROUGE scores when the input is the selected context rather than the full source, and when intent is present rather than absent. In the retrieval experiments, fine-tuned BERT bi-encoder and cross-encoder models show the same pattern in recall at 10 and MRR, and even in the large-source condition the cross-encoder often keeps the target passage in the top ten. The paper's main explanatory claim is that intent words act as a pointer that focuses prediction on the relevant part of a noisy context.

Load-bearing premise

The experiments assume that LLM-extracted partial intents behave the same as intents that real users would type: the intents were derived from the target question itself, so they are perfectly aligned with the desired output, and no independent user-supplied control was tested.

Editorial extensions

If this is right

  • Search interfaces can let users highlight any span of a page and, if the first prediction is off, add a couple of intent words to regenerate or rerank.
  • Because intent mitigates larger contexts, users need not carefully minimize their selection; a paragraph-level highlight plus a hint is nearly as good as a precise phrase.
  • Retrieval-based prediction degrades more gracefully than generation, so an answer-first interface could expose predicted passages before committing to a generated question.
  • The framework extends to retrieval-augmented generation pipelines as a query-formulation step for individual turns, with no new model architecture required.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's intents were extracted from the target question by LLMs, so the measured benefit is an upper bound; a user study with freely typed intents would reveal the real-world gap.
  • An interface could pre-generate a small set of intent suggestions from the highlighted context and let the user click one, reducing effort below typing.
  • The same context-and-intent formulation could be used to predict not just questions but broader task goals, such as which document a user will open next.
  • The observed cross-encoder robustness suggests answer retrieval could serve as a fallback when the generated question is uncertain, letting the system ask the user to choose among top passages instead of committing to a question.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The paper proposes an interactive information need prediction task in which a user selects a pre-search context (ranging from a word to a paragraph) and optionally specifies a partial search intent (e.g., "how", "why", "applications"). The authors adapt two datasets, Inquisitive and MS MARCO, by generating context and intent fields with LLMs, and evaluate both question generation (Flan-T5, Llama-3-8b, Yi-34B) and passage retrieval (bi-encoder and cross-encoder BERT) under four input conditions: Context+Intent, Source+Intent, Context, and Source. Their main findings are that larger pre-search contexts degrade prediction performance (RQ1) and that adding a partial intent improves performance, mitigating the negative effect of large contexts (RQ2). The paper concludes that the proposed framework is promising for real-world applications.

Significance. If the RQ2 result is valid, the paper identifies a genuinely novel and practical interaction paradigm: allowing users to highlight context and type a few intent words could let systems predict full questions or retrieve answers with minimal effort. The experimental coverage is broad, including both generation and retrieval models, and the paper is transparent about dataset construction details and hyperparameters. However, the central claim rests on a dataset construction that is partly circular: the "intent" field is automatically extracted from the very target question the models are asked to predict. This makes the intent benefit in RQ2 a measure of how much of a question can be recovered from a lossy summary of itself, rather than a measure of how a real user's hint would help. The promise to release code and datasets is a strength, though the actual link is absent from the manuscript.

major comments (2)
  1. [Section 4 (Building the Datasets) and Section 6.2 (RQ2)] The partial intent is not user-supplied; it is extracted from the target question using Llama-3-8b-Instruct for the Inquisitive dataset and Yi-34B-Chat for MS MARCO. The RQ2 comparisons (Context+Intent vs. Context, Source+Intent vs. Source) therefore quantify how much of the full question can be reconstructed from an automatically generated summary of that same question, rather than how a genuine user-specified hint would help. The qualitative example in Table 5 makes this concrete: the intent "hatch time" contains the content words of the target question "when do robin eggs hatch". The paper's acknowledgment that the dataset is a "silver standard" and that Inquisitive intents were not manually validated does not address this leakage. An independent control intent, such as a generic intent (e.g., "explain", "what", "why") or an intent obtained from a different annotation source (e.g., human judges), is required before the claim that "minimal specified partial search intent can help mitigate these distractions" is supported.
  2. [Section 4 (Building the Datasets) and Section 7 (Discussion)] The MS MARCO "Source" is simulated as the top BM25 paragraph retrieved for the query, not an actual pre-search context observed from user behavior. This synthetic source may not reflect the properties of real pre-search contexts (e.g., noise, topic drift, user attention). Since both RQ1 (context size) and RQ2 (intent benefit) are evaluated on this simulated source, the external validity of the conclusions is limited. The paper does state in Section 7 that user studies are future work, but the abstract and conclusion go further by claiming the framework is "suitable for real-world applications"; that claim is not supported by the current evidence.
minor comments (4)
  1. [Section 5 (Experimental Setup)] Statistical significance is assessed with Scipy's independent t-test, but the comparisons are performed on the same test samples across input conditions, so a paired test (e.g., paired t-test or Wilcoxon signed-rank test) would be more appropriate.
  2. [Section 4 (Building the Datasets)] The paper states that the code and datasets will be linked in the final version; the current manuscript contains a placeholder. Providing a public link would improve reproducibility.
  3. [Throughout] There are several typos and minor wording issues, such as "singe word" in the abstract, "potions" in Section 1, and "and and" in Section 5.1.
  4. [Section 6 (Results)] The RQ1 conclusion that larger context hurts performance is based on comparing Source vs. Context, but the Source and Context are not independent: both are derived from the same BM25 results or sentence spans. It would be useful to report inter-annotator agreement or a manual check of the constructed Context fields.

Circularity Check

1 steps flagged · score 6.0 of 10

RQ2's 'intent helps' claim is partly circular: the partial intent input is extracted from the target question itself, so the measured benefit reflects recoverability of the question from an LLM-produced summary rather than the value of an independently supplied user hint.

  1. fitted input called prediction [Section 4 (Building the Datasets) and Section 5 (Experimental Setup), input configurations (2)/(3); analyzed in Section 6.2 (RQ2)]
    "We augmented the dataset with the "Intent" by using Llama-3-8b-Instruct [15, 43] to extract the intent for each question (e..g, to extract items like "who", "why", "how many", "examples", etc.). ... Using this model, we reformulated each query into its "Context" (what the query was about) and its "Intent" (what the query is asking about the context)."

    The Intent input is generated from the target Question itself before the experiment. The RQ2 comparisons then feed "Context + Intent" or "Source + Intent" into models whose target output is that same Question. Adding Intent therefore measures how much of the question can be recovered from an LLM-produced summary of the question, not how an independently supplied user hint would help. The BLEU/ROUGE gains are partly mechanical because the extracted intent shares vocabulary with the target (e.g., Intent "hatch time" vs. Question "when do robin eggs hatch"). No control intent independent of the target was tested, so the Section 7 claim that "minimal specified partial search intent can help mitigate these distractions" is not established for real user intents.

full rationale

The paper's central RQ2 result is the claim that partial search intent helps prediction, and this is the basis for the conclusion that the proposed interactive framework is promising. The derivation chain for that result is: (1) extract Intent from the target Question using an LLM in Section 4; (2) use that Intent as an input configuration in Section 5; (3) predict the same Question; (4) observe that Intent helps. Because the intent is a lossy compression of the target question, the improvement is in part a reconstruction effect rather than evidence about how a user's independently expressed partial intent would behave. This is a genuine reduction-by-construction for the central claim. The paper does acknowledge that its adapted datasets are a "silver standard" and that the Inquisitive intent labels were not manually validated, but that limitation concerns annotation quality, not the target-question leakage that makes the intent input partially circular. The framework is not entirely circular: the Inquisitive context spans are user-selected, the MS MARCO sources come from BM25, and the retrieval baseline using the full query is an external anchor. However, the specific "intent helps" conclusion, which drives the headline takeaway, has no independent-intent control and therefore should not be read as validating user-supplied partial intents. Score 6 reflects that the main predictive claim partially reduces to its own input construction, while the rest of the study retains independent content.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claim rests primarily on dataset adaptations that are not externally validated. The two most consequential choices are the LLM-based extraction of intent from target questions and the BM25 simulation of source paragraphs. No new entities are introduced.

free parameters (2)
  • Intent extraction prompt = Not disclosed; optimized on first 100 Inquisitive validation samples
    The prompt that produces the intents used in all intent-bearing inputs is hand-tuned, and the intents directly drive the RQ2 result.
  • MS MARCO Source simulation via top-1 BM25 paragraph = Top-1 BM25 hit
    The simulated pre-search source is a single hand-chosen BM25 result, shaping all source-based inputs in the retrieval and generation experiments.
assumptions (4)
  • domain assumption LLM-extracted intents are valid proxies for the partial search intents users would type.
    The RQ2 comparison assumes that an intent extracted from the target question is representative of real user hints; never validated with users.
  • domain assumption BM25 top-1 paragraphs simulate realistic pre-search contexts.
    The MS MARCO Source is built by query-based BM25 retrieval, which may not match how users actually select context before searching.
  • domain assumption Inquisitive spans and questions can be transplanted to the interactive setting.
    The adapted Inquisitive samples assume the annotator's highlighted span corresponds to a user-selected context and the question to the full information need.
  • domain assumption ROUGE and BLEU capture the quality of information need prediction.
    Automated n-gram metrics are used to measure question generation quality without human judgments.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Interactive Information Need Prediction with Intent and Context." pith.science (2026). https://pith.science/paper/YOG6UURQ

@misc{pith2026250102635,
  author       = {Pith},
  title        = {Pith review of: Interactive Information Need Prediction with Intent and Context},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YOG6UURQ}},
  note         = {Machine review of arXiv:2501.02635}
}
read the original abstract

The ability to predict a user's information need would have wide-ranging implications, from saving time and effort to mitigating vocabulary gaps. We study how to interactively predict a user's information need by letting them select a pre-search context (e.g., a paragraph, sentence, or singe word) and specify an optional partial search intent (e.g., "how", "why", "applications", etc.). We examine how various generative language models can explicitly make this prediction by generating a question as well as how retrieval models can implicitly make this prediction by retrieving an answer. We find that this prediction process is possible in many cases and that user-provided partial search intent can help mitigate large pre-search contexts. We conclude that this framework is promising and suitable for real-world applications.

Figures

Figures reproduced from arXiv: 2501.02635 by the authors.

Figure 1
Figure 1. Demonstrating information need prediction via selected pre-search context and specified partial search intent. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The trade-off between selected pre-search context and specified search intent. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

48 extracted references · 31 canonical work pages

  1. [1]

    01. AI, :, Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, Kaidong Yu, Peng Liu, Qiang Liu, Shawn Yue, Senbin Yang, Shiming Yang, Tao Yu, Wen Xie, Wenhao Huang, Xiaohui Hu, Xiaoyi Ren, Xinyao Niu, Pengcheng Nie, Yuchi Xu, Yudong Liu, Yue Wang, Yuxuan Cai, Zhenyu Gu, Zhiyuan Liu, and...

    arXiv 2024

  2. [2]

    Marwah Alaofi, Luke Gallagher, Dana McKay, Lauren L Saling, Mark Sanderson, Falk Scholer, Damiano Spina, and Ryen W White. 2022. Where Do Queries Come From?. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2850–2862

    work page 2022

  3. [3]

    S Sathya Bama, MI Ahmed, and A Saravanan. 2015. A survey on performance evaluation measures for information retrieval system. International Research Journal of Engineering and Technology 2, 2 (2015), 1015–1020

    work page 2015

  4. [4]

    Jay Budzik and Kristian Hammond. 1999. Watson: Anticipating and contextu- alizing information needs. In Proceedings of the ASIST Annual Meeting , Vol. 36. 727–40

    work page 1999

  5. [5]

    Asli Celikyilmaz, Elizabeth Clark, and Jianfeng Gao. 2020. Evaluation of text generation: A survey. arXiv preprint arXiv:2006.14799 (2020)

    arXiv 2020

  6. [6]

    Zhicong Cheng, Bin Gao, and Tie-Yan Liu. 2010. Actively predicting diverse search intent from user browsing behaviors. In Proceedings of the 19th international conference on World wide web. 221–230

    work page 2010

  7. [7]

    Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2018. QuAC: Question answering in context. arXiv preprint arXiv:1808.07036 (2018)

    arXiv 2018

  8. [8]

    Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V

    Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Web- son, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dea...

    arXiv doi:10.48550/arxiv.2210.11416 2022

Show all 48 references
  1. [9]

    The SciPy community. 2024. scipy.stats.ttest_ind. https://docs.scipy.org/doc/ scipy/reference/generated/scipy.stats.ttest_ind.html

    2024

  2. [10]

    Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. 2022. Llm. int8 (): 8-bit matrix multiplication for transformers at scale. arXiv preprint arXiv:2208.07339 (2022)

    2022 arXiv

  3. [11]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding.arXiv preprint arXiv:1810.04805 (2018)

    2018 arXiv

  4. [12]

    Hugging Face. 2024. 01-ai/Yi-34B-Chat - Hugging Face. https://huggingface.co/01- ai/Yi-34B-Chat

    2024

  5. [13]

    Hugging Face. 2024. google-bert/bert-base-uncased - Hugging Face . https:// huggingface.co/google-bert/bert-base-uncased

    2024

  6. [14]

    Hugging Face. 2024. google/flan-t5-base - Hugging Face . https://huggingface.co/ google/flan-t5-base

    2024

  7. [15]

    Hugging Face. 2024. meta-llama/Meta-Llama-3-8B-Instruct - Hugging Face. https: //huggingface.co/google/flan-t5-base

    2024

  8. [16]

    Wei-Jen Ko, Te-yuan Chen, Yiyan Huang, Greg Durrett, and Junyi Jessy Li. 2020. Inquisitive question generation for high level text comprehension. arXiv preprint arXiv:2010.01657 (2020)

    2020 arXiv

  9. [17]

    Wei-Jen Ko, Te-yuan Chen, Yiyan Huang, Greg Durrett, and Junyi Jessy Li. 2020. Inquitive Dataset. https://github.com/wjko2/INQUISITIVE

    2020

  10. [18]

    Weize Kong, Rui Li, Jie Luo, Aston Zhang, Yi Chang, and James Allan. 2015. Predicting search intent based on pre-search context. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 503–512

    2015

  11. [19]

    Markus Koskela, Petri Luukkonen, Tuukka Ruotsalo, Mats Sjöberg, and Patrik Floréen. 2018. Proactive information retrieval by capturing search intent from primary task context. ACM Transactions on Interactive Intelligent Systems (TiiS) 8, 3 (2018), 1–25

    2018

  12. [20]

    Sunhwan Lee, Robert Moore, Guang-Jie Ren, Raphael Arar, and Shun Jiang

  13. [21]

    Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing S...

    2020

  14. [22]

    Hang Li, Jun Xu, et al . 2014. Semantic matching in search. Foundations and Trends® in Information Retrieval 7, 5 (2014), 343–469

    2014

  15. [23]

    Daniel J Liebling, Paul N Bennett, and Ryen W White. 2012. Anticipatory search: using context to initiate search. InProceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval . 1035–1036

    2012

  16. [24]

    Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021. Pyserini: A Python toolkit for reproducible infor- mation retrieval research with sparse and dense representations. In Proceedings of the 44th International ACM SIGIR Conferenc...

    2021

  17. [25]

    Jimmy Lin, Rodrigo Nogueira, and Andrew Yates. 2022. Pretrained transformers for text ranking: Bert and beyond . Springer Nature

    2022

  18. [26]

    Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Comput. Surveys 55, 9 (2023), 1–35

    2023

  19. [27]

    Zakariae ALAMI MERROUNI, Bouchra FRIKH, and Brahim OUHBI. 2019. Toward contextual information retrieval: a review and trends. Procedia computer science 148 (2019), 191–200

    2019

  20. [28]

    Microsoft. 2024. MS MARCO. https://microsoft.github.io/msmarco/

    2024

  21. [29]

    Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. choice 2640 (2016), 660

    2016

  22. [30]

    PyTorch. 2024. CosineEmbeddingLoss - PyTorch 2.3 Documentation . https:// huggingface.co/google-bert/bert-base-uncased

    2024

  23. [31]

    Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9

    2019

  24. [32]

    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21, 1 (2020), 5485–5551

    2020

  25. [33]

    Mandar Rahurkar and Silviu Cucerzan. 2008. Predicting when browsing context is relevant to search. In Proceedings of the 31st Annual international ACM SIGIR Conference on Research and Development in information Retrieval . 841–842

    2008

  26. [34]

    Siva Reddy, Danqi Chen, and Christopher D Manning. 2019. Coqa: A conversa- tional question answering challenge. Transactions of the Association for Compu- tational Linguistics 7 (2019), 249–266

    2019

  27. [35]

    Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Em- pirical Methods in Natural Language Processing . Association for Computational Linguistics. https://arxiv.org/abs/1908.10084

    2019 arXiv

  28. [36]

    Bradley Rhodes and Thad Starner. 1996. Remembrance Agent: A continuously running automated information retrieval system. In The proceedings of the first international conference on the practical application of intelligent agents and multi agent technology, Vol. 1. 487–495

    1996

  29. [37]

    Stephen E Robertson and Steve Walker. 1994. Some simple effective approxi- mations to the 2-poisson model for probabilistic weighted retrieval. In SIGIR’94: Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retr...

    1994

  30. [38]

    Mark D Smucker, James Allan, and Ben Carterette. 2007. A comparison of statistical significance tests for information retrieval evaluation. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management. 623–632

    2007

  31. [39]

    Yang Song and Qi Guo. 2016. Query-less: Predicting task repetition for nextgen proactive search and recommendation engines. In Proceedings of the 25th Interna- tional Conference on World Wide Web. 543–553. Conference’17, July 2017, Washington, DC, USA TBD, et al

    2016

  32. [40]

    Saedeh Tahery and Saeed Farzi. 2020. Customized query auto-completion and suggestion—A review. Information Systems 87 (2020), 101415

    2020

  33. [41]

    Robert S Taylor. 1962. The process of asking questions. American documentation 13, 4 (1962), 391–396

    1962

  34. [42]

    Ghazaleh H Torbati, Andrew Yates, and Gerhard Weikum. 2021. You get what you chat: Using conversations to personalize search-based recommendations. In Advances in Information Retrieval: 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28–April 1, 2021, ...

    2021

  35. [43]

    Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yas- mine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhos- ale, et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)

    2023 arXiv

  36. [44]

    Christophe Van Gysel. 2017. Remedies against the vocabulary gap in information retrieval. arXiv preprint arXiv:1711.06004 (2017)

    2017 arXiv

  37. [45]

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)

    2017

  38. [46]

    Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J

    Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jar- rod Millman, Nikolay Mayorov, Andrew R. J. Nelson,...

    2020

  39. [47]

    Ryen W White, Paul N Bennett, and Susan T Dumais. 2010. Predicting short-term interests using activity-based search context. In Proceedings of the 19th ACM international conference on Information and knowledge management . 1009–1018

    2010

  40. [2018]

    In Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence

    Making personalized recommendation through conversation: Architecture design and recommendation methods. In Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.