A Serendipity-Oriented Greedy Algorithm for Recommendations

Abstract

Abstract:Most recommender systems suggest items to a user that are popular among all users and similar to items the user usually consumes. As a result, a user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected, i.e. serendipitous items. In this paper, we propose a serendipity-oriented algorithm, which improves serendipity through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm and compare it with others, we employ a serendipity metric that captures each component of serendipity, unlike the most common metric.

How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm

“…Some researchers require items to be relevant and unexpected to be considered serendipitous [19,22], whereas other researchers suggest that serendipitous items are novel and unexpected [19,32]. However, the most common definition of serendipity includes all three components: relevance, novelty and unexpectedness [17,18].…”

“…Some researchers consider novel and unexpected items serendipitous [32], while others suggest that serendipitous items are relevant and unexpected [22]. Although there is no agreement on the definition of serendipity [19], in this paper, the term serendipitous items refers to items relevant, novel and unexpected to a user [17][18][19]:…”

“…To increase the number of items that will receive high ratings most recommender systems tend to suggest items that are (a) popular, as these items are consumed by many individuals and are often of high quality in many domains [5] and (b) similar to those the user has assigned high ratings, as these items correspond to users' preferences [17,19,29]. As a result, users might become bored with the suggestions provided, as (1) users are likely to be familiar with popular items, while the main reason these users would use a recommender system is to find novel and relevant items [5] and (b) users often lose interest in using the system when they are offered only items similar to items from their profiles (the so-called overspecialization problem) [17][18][19]29]. Here the term user profile refers to the unique ID and the set of items rated by the target user [17], though it might include information, such as real name, user name and age in other papers.…”

“…-An item is novel to a user if the user had not heard of this item or had not thought of this item prior to the recommendation of this item [16]. Items novel to a user are usually unpopular, as users are often familiar with popular items, where popularity can be measured by the number of ratings given to it in the system [5,[17][18][19]. For example, a user is more likely to be familiar with the popular movie "The Shawshank Redemption" than with the unpopular movie "Coherence".…”

To Your Surprise: Identifying Serendipitous Collaborators

“…Kotkov et al [12] reviewed and classified most important serendipityrelated recommendation algorithms explicitly. Later, they proposed a greedy recommendation algorithm [25] for providing relevant, novel and unexpected items, which is based on the topic diversification. Zhang et al [26] maximized the diversity of the recommendation list and kept adequate accuracy simultaneously for avoiding monotony of the recommendation list.…”