Topic diversity in tag recommendation

Belém, Fabiano; Santos, Rodrygo L. T.; Almeida, Jussara M.; Gonçalves, Marcos André

doi:10.1145/2507157.2507184

Cited by 19 publications

(22 citation statements)

References 25 publications

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“…This interpretation only cares pairwise dissimilarity between each item. (2) Item group representative diversity, items that represent each of dissimilar groups of similar items in inventory as many as possible should be placed in the RecList [7]. For instance, a movie recommender focused on (2) recommends movies in a variety of representative genres such as action, comedy, romance in a single RecList while a recommender focused on (1) can recommend movies in various sub-genres in action.…”

Section: Accuracymentioning

confidence: 99%

“…The subtopic set S is defined by the application requirement. If the objective is recommending a diversity of popular genres, then S is equivalent to the set of popular movie genres [7], [77]. If an application aims to cover as much as possible a movie genre watched by a target u, then S is equivalent to the set of the movie genres watched by u [84].…”

Section: Ild(r)mentioning

confidence: 99%

“…The thinking behind the algorithms has been introduced to the recommendation field, through several studies [7], [76], [79].…”

Section: Relation To Other Information-retrieval Domainsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Recommendation Methods Beyond Accuracy

Han

Yamana

2017

IEICE Trans. Inf. & Syst.

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SUMMARYIn recommending to another individual an item that one loves, accuracy is important, however in most cases, focusing only on accuracy generates less satisfactory recommendations. Studies have repeatedly pointed out that aspects that go beyond accuracy-such as the diversity and novelty of the recommended items-are as important as accuracy in making a satisfactory recommendation. Despite their importance, there is no global consensus about definitions and evaluations regarding beyondaccuracy aspects, as such aspects closely relate to the subjective sensibility of user satisfaction. In addition, devising algorithms for this purpose is difficult, because algorithms concurrently pursue the aspects in trade-off relation (i.e., accuracy vs. novelty). In the aforementioned situation, for researchers initiating a study in this domain, it is important to obtain a systematically integrated view of the domain. This paper reports the results of a survey of about 70 studies published over the last 15 years, each of which addresses recommendations that consider beyond-accuracy aspects. From this survey, we identify diversity, novelty, and coverage as important aspects in achieving serendipity and popularity unbiasedness-factors that are important to user satisfaction and business profits, respectively. The five major groups of algorithms that tackle the beyond-accuracy aspects are multi-objective, modified collaborative filtering (CF), clustering, graph, and hybrid; we then classify and describe algorithms as per this typology. The off-line evaluation metrics and user studies carried out by the studies are also described. Based on the survey results, we assert that there is a lot of room for research in the domain. Especially, personalization and generalization are considered important issues that should be addressed in future research (e.g., automatic per-user-trade-off among the aspects, and properly establishing beyond-accuracy aspects for various types of applications or algorithms).

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Section: Accuracymentioning

confidence: 99%

Section: Ild(r)mentioning

confidence: 99%

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A Survey on Recommendation Methods Beyond Accuracy

Han

Yamana

2017

IEICE Trans. Inf. & Syst.

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“…In [91], a co-occurrence analysis is used to mine the top frequent tags for songs from social tagging web sites, and topic modelling is used to determine a set of latent topics for each song. Recently, more techniques for context modeling were developed [27,92,224].…”

Section: Context-aware Recommendationsmentioning

confidence: 99%

Using Contextual Information to Understand Searching and Browsing Behavior

Kiseleva

2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…We consider relevance (i.e., how well the result set of tags describes an item to a user), coverage (i.e., how well the result set of tags covers the diverse aspects of an item), and polarity (i.e., how well sentiment is attached to the result set of tags) in order to enable a user to satisfactorily review an item. Though relevance and coverage have been studied in the past [4], our work is the first to consider all three measures simultaneously in the context of tag mining, to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

The TagAdvisor

Nazi

Das

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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The increasing popularity and widespread use of online review sites over the past decade has motivated businesses of all types to possess an expansive arsenal of user feedback (preferably positive) in order to mark their reputation and presence in the Web. Though a significant proportion of purchasing decisions today are driven by average numeric scores (e.g., movie rating in IMDB), detailed reviews are critical for activities such as buying an expensive digital SLR camera, reserving a vacation package, etc. Since writing a detailed review for a product (or, a service) is usually time-consuming and may not offer any incentive, the number of useful reviews available in the Web is far from many. The corpus of reviews available at our disposal for making informed decisions also suffers from spam and misleading content, typographical and grammatical errors, etc. In this paper, we address the problem of how to engage the lurkers (i.e., people who read reviews but never take time and effort to write one) to participate and write online reviews by systematically simplifying the reviewing task. Given a user and an item that she wants to review, the task is to identify the top-k meaningful phrases (i.e., tags) from the set of all tags (i.e., available user feedback for items) that, when advised, would help her review an item easily. We refer to it as the TagAdvisor problem, and formulate it as a generalconstrained optimization goal. Our framework is centered around three measures -relevance (i.e., how well the result set of tags describes an item to a user), coverage (i.e., how well the result set of tags covers the different aspects of an item), and polarity (i.e., how well sentiment is attached to the result set of tags) in order to help a user review an item satisfactorily. By adopting different definitions of coverage, we identify two concrete problem instances that enable a wide range of real-world scenarios. We show that these problems are NP-hard and develop practical algorithms with theoretical bounds to solve them efficiently. We conduct detailed experiments on synthetic and real data crawled from the web to validate the utility of our problem and effectiveness of our solutions.

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Topic diversity in tag recommendation

Cited by 19 publications

References 25 publications

A Survey on Recommendation Methods Beyond Accuracy

A Survey on Recommendation Methods Beyond Accuracy

Using Contextual Information to Understand Searching and Browsing Behavior

The TagAdvisor

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