Query: "recommendation" serendipity

title: Directional and Explainable Serendipity Recommendation
abstract: Serendipity recommendation has attracted more and more attention in recent years; it is committed to providing recommendations which could not only cater to users' demands but also broaden their horizons. However, existing approaches usually measure user item relevance with a scalar instead of a vector, ignoring user preference direction, which increases the risk of unrelated recommendations. In addition, reasonable explanations increase users' trust and acceptance, but there is no work to provide explanations for serendipitous recommendations. To address these limitations, we propose a Directional and Explainable Serendipity Recommendation method named DESR. Specifically, we extract users' long term preferences with an unsupervised method based on GMM (Gaussian Mixture Model) and capture their short term demands with the capsule network at first. Then, we propose the serendipity vector to combine long term preferences with short term demands and generate directionally serendipitous recommendations with it. Finally, a back routing scheme is exploited to offer explanations. Extensive experiments on real world datasets show that DESR could effectively improve the serendipity and explainability, and give impetus to the diversity, compared with existing serendipity based methods.

title: Neural Serendipity Recommendation
abstract: 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.

title: Serendipity adjustable application recommendation via joint disentangled recurrent variational auto encoder
abstract: In this study, we propose an advanced mobile application recommendation method that can systematically adjust the level of serendipity. Previous studies focused on generating recommendations that are relevant to users' interests. However, this leads to a lack of serendipity and novelty for the user and is therefore not sufficient to achieve high levels of user satisfaction. Our model is trained to predict the next application to be recommended based on a disentangled representation of the sequence in which applications were previously used. We introduce variety into our model by varying the attributes of the disentangled representation. The results of our experiments indicate that the proposed method can systematically and effectively adjust the level of serendipity by varying the values and dimensions of the attribute variables in the disentangled representation. Additionally, our method produces recommendations that are superior to those of other benchmark methods in terms of serendipity and relevance.

title: Serendipity and Locality for Audio Recommendation
abstract: This dissertation describes a novel model for serendipitous audio recommendation, called SyLAR (Serendipity and Locality for Audio Recommendation) that goes beyond improving accuracy based on the user preferences. In addition to the emphasis on user predictability and anomalous patterns in item collections, it exploits the prevailing spatial contexts of the audio recommendation. Based on the model, we propose several recommender algorithms in both personalized and non personalized settings.

title: CHESTNUT: Improve Serendipity in Movie Recommendation by an Information Theory Based Collaborative Filtering Approach
abstract: 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:/github.com/unnc ucc/CHESTNUT.

title: Designing for serendipity in a university course recommendation system
abstract: Collaborative filtering based algorithms, including Recurrent Neural Networks (RNN) tend towards predicting a perpetuation of past observed behavior. In a recommendation context, this can lead to an overly narrow set of suggestions lacking in serendipity and inadvertently placing the user in what is known as a "filter bubble." In this paper, we grapple with the issue of the filter bubble in the context of a course recommendation system in production at a public university. Our approach is to present course results that are novel or unexpected to the student but still relevant to their interests. We build one set of models based on course catalog descriptions (BOW) and another set informed by enrollment histories (course2vec) We compare the performance of these models on off line validation sets and against the system's existing RNN based recommendation engine in an online user study of undergraduates (N 70) who rated their course recommendations along six characteristics related to serendipity. Results of the user study show a dramatic lack of novelty in RNN recommendations and depict the characteristic trade offs that make serendipity difficult to achieve. While the machine learned course2vec models performed best on off line validation tasks, it was the simple bag of words based recommendations that students rated as more serendipitous. We discuss the role of the kind of information presented by the system in a student's decision to accept a recommendation from either algorithm.

title: Neural Serendipity Recommendation
abstract: 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. Usu.

title: HAES: A New Hybrid Approach for Movie Recommendation with Elastic Serendipity
abstract: Recommendation systems provide good guidance for users to find their favorite movies from an overwhelming amount of options. However, most systems excessively pursue the recommendation accuracy and give rise to over specialization, which triggers the emergence of serendipity. Hence, serendipity recommendation has received more attention in recent years, facing three key challenges: subjectivity in the definition, the lack of data, and users' floating demands for serendipity. To address these challenges, we introduce a new model called HAES, a H ybrid A pproach for movie recommendation with E lastic S erendipity, to recommend serendipitous movies. Specifically, we (1) propose a more objective definition of serendipity, \em content difference and \em genre accuracy, according to the analysis on a real dataset, (2) propose a new algorithm named JohnsonMax to mitigate the data sparsity and build weak ties beneficial to finding serendipitous movies, and (3) define a novel concept of elasticity in the recommendation, to adjust the level of serendipity flexibly and reach a trade off between accuracy and serendipity. Extensive experiments on real world datasets show that HAES enhances the serendipity of recommendations while preserving recommendation quality, compared to several widely used methods.

title: A serendipity biased Deepwalk for collaborators recommendation
abstract: Scientific collaboration has become a common behaviour in academia. Various recommendation strategies have been designed to provide relevant collaborators for the target scholars. However, scholars are no longer satisfied with the acquainted collaborator recommendations, which may narrow their horizons. Serendipity in the recommender system has attracted increasing attention from researchers in recent years. Serendipity traditionally denotes the faculty of making surprising discoveries. The unexpected and valuable scientific discoveries in science such as X rays and penicillin may be attributed to serendipity. In this paper, we design a novel recommender system to provide serendipitous scientific collaborators, which learns the serendipity biased vector representation of each node in the co author network. We first introduce the definition of serendipitous collaborators from three components of serendipity: relevance, unexpectedness, and value, respectively. Then we improve the transition probability of random walk in DeepWalk, and propose a serendipity biased DeepWalk, called Seren2vec. The walker jumps to the next neighbor node with the proportional probability of edge weight in the co author network. Meanwhile, the edge weight is determined by the three indices in definition. Finally, Top N serendipitous collaborators are generated based on the cosine similarity between scholar vectors. We conducted extensive experiments on the DBLP data set to validate our recommendation performance, and the evaluations from serendipity based metrics show that Seren2vec outperforms other baseline methods without much loss of recommendation accuracy.

title: User Validation of Recommendation Serendipity Metrics
abstract: Though it has been recognized that recommending serendipitous (i.e. surprising and relevant) items can be helpful for increasing users' satisfaction and behavioral intention, how to measure serendipity in the offline environment is still an open issue. In recent years, a number of metrics have been proposed, but most of them were based on researchers' assumptions due to the serendipity's subjective nature. In order to validate these metrics' actual performance, we collected over 10,000 users' real feedback data and compared with the metrics' results. It turns out the user profile based metrics, especially content based ones, perform better than those based on item popularity, in terms of estimating the unexpectedness facet of recommendations. Moreover, the full metrics, which involve the unexpectedness component, relevance, timeliness, and user curiosity, can more accurately indicate the recommendation's serendipity degree, relative to those that just involve some of them. The application of these metrics to several recommender algorithms further consolidates their practical usage, because the comparison results are consistent with those from user evaluation. Thus, this work is constructive for filling the gap between offline measurement and user study on recommendation serendipity.