Collaborative filtering (CF) algorithms have been widely used to build recommender systems since they have distinguishing capability of sharing collective wisdoms and experiences. However, they may easily fall into the trap of the Matthew effect, which tends to recommend popular items and hence less popular items become increasingly less popular. Under this circumstance, most of the items in the recommendation list are already familiar to users and therefore the performance would seriously degenerate in finding cold items, i.e., new items and niche items. To address this issue, in this paper, a user survey is first conducted on the online shopping habits in China, based on which a novel recommendation algorithm termed innovator-based CF is proposed that can recommend cold items to users by introducing the concept of innovators. Specifically, innovators are a special subset of users who can discover cold items without the help of recommender system. Therefore, cold items can be captured in the recommendation list via innovators, achieving the balance between serendipity and accuracy. To confirm the effectiveness of our algorithm, extensive experiments are conducted on the dataset provided by Alibaba Group in Ali Mobile Recommendation Algorithm Competition, which is collected from the real e-commerce environment and covers massive user behavior log data.

Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering

A recommender system is employed to accurately recommend items, which are expected to attract the user’s attention. The over-emphasis on the accuracy of the recommendations can cause information over-specialization and make recommendations boring and even predictable. Novelty and diversity are two partly useful solutions to these problems. However, novel and diverse recommendations cannot merely ensure that users are attracted since such recommendations may not be relevant to the user’s interests. Hence, it is necessary to consider other criteria, such as unexpectedness and relevance. Serendipity is a criterion for making appealing and useful recommendations. The usefulness of serendipitous recommendations is the main superiority of this criterion over novelty and diversity. The bulk of studies of recommender systems have focused on serendipity in recent years. Thus, a systematic literature review is conducted in this paper on previous studies of serendipity-oriented recommender systems. Accordingly, this paper focuses on the contextual convergence of serendipity definitions, datasets, serendipitous recommendation methods, and their evaluation techniques. Finally, the trends and existing potentials of the serendipity-oriented recommender systems are discussed for future studies. The results of the systematic literature review present that the quality and the quantity of articles in the serendipity-oriented recommender systems are progressing.

Serendipity in Recommender Systems: A Systematic Literature Review

Recommender systems have been playing an important role in providing personalized information to users. However, there is always a trade-off between accuracy and novelty in recommender systems. Usually, many users are suffering from redundant or inaccurate recommendation results. To this end, in this article, we put efforts into exploring the hidden knowledge of observed ratings to alleviate this recommendation dilemma. Specifically, we utilize some basic concepts to define a concept, Serendipity, which is characterized by high-satisfaction and low-initial-interest. Based on this concept, we propose a two-phase recommendation problem which aims to strike a balance between accuracy and novelty achieved by serendipity prediction and personalized recommendation. Along this line, a Neural Serendipity Recommendation (NSR) method is first developed by combining Muti-Layer Percetron and Matrix Factorization for serendipity prediction. Then, a weighted candidate filtering method is designed for personalized recommendation. Finally, extensive experiments on real-world data demonstrate that NSR can achieve a superior serendipity by a 12% improvement in average while maintaining stable accuracy compared with state-of-the-art methods.

Neural Serendipity Recommendation: Exploring the Balance between Accuracy and Novelty with Sparse Explicit Feedback

Despite the potential importance of emotional aspects in information seeking, there is a lack of adequate attention to emotions' role in facilitating serendipitous information encountering. This paper contributes to this research gap by investigating the role of emotions during the process of perceiving and experiencing serendipitous information encountering in a controlled laboratory setting. The results show that applying a sketch game can stimulate participants' emotions. Our findings indicate that participants are more likely to experience serendipitous information encountering under the influence of positive emotions. This study contributes to an understanding of the relationship between emotions and the perception of serendipitous information encountering. The implications of the possibilities of facilitating positive emotions to induce serendipitous information encountering are discussed.

Investigating the impact of emotions on perceiving serendipitous information encountering

The term “serendipity” has been understood narrowly in the Recommender System Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity In this paper, we introduce CHESTNUT, a memory-based movie collaborative filtering system to improve serendipity performance Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous recommender system With lightweight experiments, we have revealed a few runtime issues and further optimized the same We have evaluated CHESTNUT in both practicability and effectiveness, and the results show that it is fast, scalable and improves serendipity performance significantly, compared with mainstream memory-based collaborative filtering The source codes of CHESTNUT are online at https://githubcom/unnc-ucc/CHESTNUT

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

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.

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Zhan Xu

Papers

A New Information Theory-Based Serendipitous Algorithm Design