Challenges of Serendipity in Recommender Systems

Abstract

Abstract:Most recommender systems suggest items similar to a user profile, which results in boring recommendations limited by user preferences indicated in the system. To overcome this problem, recommender systems should suggest serendipitous items, which is a challenging task, as it is unclear what makes items serendipitous to a user and how to measure serendipity. The concept is difficult to investigate, as serendipity includes an emotional dimension and serendipitous encounters are very rare. In this paper, we discuss mentioned challenges, review definitions of serendipity and serendipity-oriented evaluation metrics. The goal of the paper is to guide and inspire future efforts on serendipity in recommender systems.

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

“…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]:…”

“…Recommender systems are software tools that suggest items of use to users [17,27]. An item is "a piece of information that refers to a tangible or digital object, such as a good, a service or a process that a recommender system suggests to the user in an interaction through the Web, email or text message" [17]. For example, an item could refer to a movie, a song or a new friend.…”

“…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.…”

“…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.…”

Remote Experimentation supported by Learning Analytics and Recommender Systems

“…Based on these arguments, [21] state that "the purpose of RS is to generate valid recommendations for items that may be of interest to a set of users". As mentioned by [22], an "item" refers to something tangible or a digital object, such as a product, a service, or a process within the scope of recommendation of an RS to the user considering their interaction with some media. According to [23], "item" is the general term that designates what the system recommends to users.…”