Understanding How People Use Natural Language to Ask for Recommendations

Kang, Jie; Condiff, Kyle; Chang, Shuo; Konstan, Joseph A.; Terveen, Loren; Harper, F. Maxwell

doi:10.1145/3109859.3109873

Cited by 52 publications

(35 citation statements)

References 25 publications

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“…DFT addresses both the choice context and the temporal dependency of choices. This directly matches the scenario in screen-based recommender systems where users browse items page by page and make decisions of action or not (note that in recently studied voice-driven recommender systems [7,16], this process however may not apply).…”

Section: Introductionsupporting

confidence: 65%

From preference into decision making

Zhao

Willemsen

Adomavicius

et al. 2019

Proceedings of the 13th ACM Conference on Recommender Systems

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User-system interaction in recommender systems involves three aspects: temporal browsing (viewing recommendation lists and/or searching/fltering), action (performing actions on recommended items, e.g., clicking, consuming) and inaction (neglecting or skipping recommended items). Modern recommenders build machine learning models from recordings of such user interaction with the system, and in doing so they commonly make certain assumptions (e.g., pairwise preference orders, independent or competitive probabilistic choices, etc.). In this paper, we set out to study the efects of these assumptions along three dimensions in eight diferent single models and three associated hybrid models on a user browsing data set collected from a real-world recommender system application. We further design a novel model based on recurrent neural networks and multi-task learning, inspired by Decision Field Theory, a model of human decision making. We report on precision, recall, and MAP, fnding that this new model outperforms the others. CCS CONCEPTS • Information systems → Recommender systems.

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Section: Introductionsupporting

confidence: 65%

From preference into decision making

Zhao

Willemsen

Adomavicius

et al. 2019

Proceedings of the 13th ACM Conference on Recommender Systems

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“…Adaptive place advisor [31] provides personalized recommendations to assist users to find preferable places for traveling by considering both users' long-term preferences and short-term interests. Also, several studies show the superiority of conversational user interfaces over graphical user interfaces during the process of recommendations [10,15,38].…”

Section: Conversational Recommender Systemsmentioning

confidence: 99%

Critiquing for Music Exploration in Conversational Recommender Systems

Cai

Jin

Chen

2021

26th International Conference on Intelligent User Interfaces

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Dialogue-based conversational recommender systems allow users to give language-based feedback on the recommended item, which has great potential for supporting users to explore the space of recommendations through conversation. In this work, we consider incorporating critiquing techniques into conversational systems to facilitate users' exploration of music recommendations. Thus, we have developed a music chatbot with three system variants, which are respectively featured with three different critiquing techniques, i.e., user-initiated critiquing (UC), progressive system-suggested critiquing (Progressive SC), and cascading system-suggested critiquing (Cascading SC). We conducted a between-subject study (N=107) to compare these three types of systems with regards to music exploration in terms of user perception and user interaction. Results show that both UC and SC are useful for music exploration, while users perceive higher diversity of recommendations with the system that offers Cascading SC and perceive more serendipitous with the system that offers Progressive SC. In addition, we find that the critiquing techniques significantly moderate the relationships between some interaction metrics (e.g., number of listened songs, number of dialogue turns) and users' perceived helpfulness and serendipity during music exploration. CCS CONCEPTS• Human-centered computing → User interface design; Empirical studies in interaction design; User studies; • Information systems → Recommender systems.

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“…Within information retrieval, Bogers and Koolen (2018) approach book recommendation with Narrative-Driven Recommendation (NDR), using user narratives from the online book discussion forums as natural language book recommendation requests. Kang et al (2017) studies user queries for movie recommendations, highlighting not only the range of linguistic variation in such a recommendation task, but also how Wit.ai 1 can effectively tackle NLU in a recommendation task: their slots were retrieved from user input reliably. (B. Rex also uses Wit.ai.…”

Section: Related Workmentioning

confidence: 99%

B. Rex: a dialogue agent for book recommendations

Abrams

Gessler

Marge

2019

Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

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Information overload can be challenging for children searching for books among a multitude of titles, authors, and genres. We present B. Rex, a dialogue agent for book recommendations. B. Rex aims to exploit the cognitive ease of natural dialogue and the excitement of a whimsical persona in order to engage users who might not enjoy using more common interfaces for finding new books. B. Rex succeeds in making book recommendations with good quality based on only information revealed by the user in the dialogue.

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Understanding How People Use Natural Language to Ask for Recommendations

Cited by 52 publications

References 25 publications

From preference into decision making

From preference into decision making

Critiquing for Music Exploration in Conversational Recommender Systems

B. Rex: a dialogue agent for book recommendations

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