A New Information Theory-Based Serendipitous Algorithm Design

Abstract

The development of information technology has stimulated an increasing number of researchers to investigate how to provide serendipitous experience to users in the digital environment, especially in the fields of information research and recommendation systems. Although a number of achievements have been made in understanding the nature of serendipity in the context of information research, few of these achievements have been employed in the design of information systems. This paper proposes a new serendipitous recommendation algorithm based on previous empirical studies by taking into considerations of the three important elements of serendipity, namely "unexpectedness", "insight" and "value". We consider our design of the algorithm as an important attempt to bridge the research fruits between the two areas of information research and recommendation systems. By applying the designed algorithm to a game-based application in a real life experiment with target users, we have found that comparing to the conventional designed method; the proposed algorithm has successfully provided more possibilities to the participants to experience serendipitous encountering.

CHESTNUT: Improve Serendipity in Movie Recommendation by an Information Theory-Based Collaborative Filtering Approach

“…More recently, previous research has highlighted how making connections is an important point for serendipitous engineering [13]. Based on previous research outcome from Information Research, an Information Theory-based algorithm has been proposed to better understand serendipity in the Recommender System [36]. Furthermore, a systematic context-based study among Chinese Scholars has been conducted and proves the effectiveness of the proposed algorithm [35].…”

“…The understanding of serendipity, as a user-centered concept, has been a gap for a while. Until recently, an awareness of this gap has led a conceptual bridge, which introduced serendipity from Information Research into Recommender Systems, by proposing an Information Theory-based algorithm [36]. To further investigate this algorithm, it needs to be implemented as an end-to-end recommender system, but it is difficult to do so.…”

“…(1) CHESTNUT Movie Recommender System. CHESTNUT applies an Information Theory-based algorithm, which aims to combine three key metrics based on a user-centered understanding of serendipity: insight, unexpectedness and value [36]. With regard to these metrics, CHESTNUT has three key functional units, respectively: 1) cInsight performs the making connections to expand a target user's profile, to collect all target-user-related items (Section 3.1); 2) cUnexpectedness filtered out all expected items from all target-userrelated items, with the help of a primitive prediction model (Section 3.2); and 3) cUsefulness evaluates the potential value of those candidate items through prediction, and generates a list of recommendations by sorting them from high to low (Section 3.3).…”

Early Findings from a Large-Scale User Study of CHESTNUT: Validations and Implications

“…Although the theoretical support of CHESTNUT [24], its effectiveness [23] and practical system performance [16] has been examined earlier, the missing validation from real-world users is still missing. Also, a large-scale user study would also help to uncover several issues, which are not capable to be found through off-line evaluations, and enhance its practicality.…”

“…Towards a more comprehensive understanding of serendipity, we have built CHESTNUT , the first serendipitous movie recommender system with an Information Theory-based algorithm, to embed a more comprehensive understanding of serendipity in a practical recommender system [16,24]. Although experimental studies on static data sets have shown that CHESTNUT could achieve significant improvements (i.e.…”