A Survey on Recommendation Methods Beyond Accuracy

Abstract

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).

News recommender system: a review of recent progress, challenges, and opportunities

“…Coverage represents the percentage of distinct items/users/ratings that a recommender system can recommend. Popular interpretations of coverage include item coverage (percentage of items), user coverage (percentage of users), catalog coverage (percentage of recommended user-item pairs) and interaction coverage (rating predictions) over potential items, users, user-item pairs, or ratings respectively (Han and Yamana 2017 ). Coverage in an NRS is treated no differently than that in other recommendation domains.…”

Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship

“…Existing work, conceptualizing diversity in this way, even if it is concerned with and sensitive to the broader social role of cultural content, does not adequately support the rich set of goals system designers might have and the values they might want to implement through design. Typically, diversity metrics are limited to the goal of capturing the variety of content within a recommendation list; they may consider categorizations of the content, distances in a latent space, or simply how many different items are recommended [2,43,51,82]. Aligned with the goals of personalization, the formulation of these diversity metrics sometimes optionally consider the relevance of the content for users, assuming that what a user consumed in the past indicates what they will still be interested in and that recommendations should be limited to such categories.…”

A Closer-to-Reality Model for Comparing Relevant Dimensions of Recommender Systems, with Application to Novelty

“…The latter are considered useful and favored in the neighborhood constitution. The proposed technique aims at increasing novelty, coverage, and diversity [54].…”