Introducing Serendipity in a Content-Based Recommender System

Abstract

Today recommenders are commonly used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based recommenders rely on the concept of similarity between the bought/searched/visited item and all the items stored in a repository. It is a common belief that the user is interested in what issimilar to what she has already bought/searched/visited. We believe that there are some contexts in which this assumption is wrong: it is the case of acquiring unsearched but still useful items or pieces of information. This is called serendipity. Our purpose is to stimulate users and facilitate these serendipitous encounters to happen. This paper presents the design and implementation of a hybrid recommender system that joins a contentbased approach and serendipitous heuristics in order to mitigate the over-specialization problem with surprising suggestions. Background and MotivationInformation overload is a common issue among the modern information society. Information Filtering (IF) is a kind of intelligent computing techniques that mitigates this problem by providing the user with the most relevant information with respect to her information needs.Recommender systems (RSs) adopt IF techniques in order to provide customized information access for targeted domains.They can be viewed as intelligent systems that take input directly or indirectly from users and, based on their needs, preferences and usage patterns, provide personalized advices about products or services and can help people to filter useful information.Several definitions of RS have been given. According to [3]: "Recommender systems have the effect of guiding the user in a personalized way to interesting or useful objects in a large space of possible options". This definition makes it clear that user oriented guidance is critical in a RS.Among different recommendation techniques proposed in the literature, the content-based and the collaborative filtering approaches are the most widely adopted to date. Systems implementing the contentbased recommendation approach analyze a set of documents, usually textual descriptions of the items previously rated by an individual user, and build a model or profile of user interests based on the features of the objects rated by that user [14]. The profile is exploited to recommend new items of interest. Collaborative recommenders differ from content-based ones in that user opinions are used instead of content. They gather ratings about objects by users and store them in a centralized or distributed database. To provide user X with recommendations, the system computes the neighborhood of that user, i.e. the subset of users that have a taste similar to X. Similarity in taste is computed based on the similarity of ratings for objects that were rated by both users. The system then recommends objects that users in X's neighborhood indicated to like, provided that they have not yet been rated by X. Each type of filtering methods has its own weaknesses and strengths.In particular, the content-ba...

How good your recommender system is? A survey on evaluations in recommendation

“…The authors use the word serendipity as a concept of surprising and interesting item for the user. The same is stated by Iaquinta et al [17], where they mention serendipity represent items that the users would difficultly find. Moreover, Ge et al [12] define serendipitous items as surprising and pleasant.…”

“…In a simplified way, a novel item for a user is one that the user has none or little knowledge about [12]. Herlocker et al [14], Iaquinta et al [16,17] state that novelty is when a RS predicts items that the user does not know about and might not discover by other sources. Moreover, novelty has also been defined as how different the recommended item is when compared towards what the user has consumed [36].…”

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

“…The situation is termed as serendipity. Incorporating serendipitous recommendation strategy with reclusive methods alleviate the over specialization problems in recommendation [131]. The authors have suggested hybrid recommendation approach to recommend surprisingly the new items to users.…”

Penguins in sweaters, or serendipitous entity search on user-generated content

“…Besides being relevant to the query, the results must be interesting enough to the user to catch his or her attention, and to encourage further exploration. Although highly subjective, the dimension of interestingness has been used to measure the serendipity of web search results [2] and recommender systems [18]. To make sure we separate intrinsic interestingness of entities from the extent to which a user interested in a search query is interested in a presented result, we ask labelers to consider both questions.…”