Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas.

Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges

We present an unsupervised segmentation algorithm which uses Markov random field models for color textures. These models characterize a texture in terms of spatial interaction within each color plane and interaction between different color planes. The models are used by a segmentation algorithm based on agglomerative hierarchical clustering. At the heart of agglomerative clustering is a stepwise optimal merging process that at each iteration maximizes a global performance functional based on the conditional pseudolikelihood of the image. A test for stopping the clustering is applied based on rapid changes in the pseudolikelihood. We provide experimental results that illustrate the advantages of using color texture models and that demonstrate the performance of the segmentation algorithm on color images of natural scenes. Most of the processing during segmentation is local making the algorithm amenable to high performance parallel implementation. >

Markov random field models for unsupervised segmentation of textured color images

What makes a good recommendation or good list of recommendations?Research into recommender systems has traditionally focused on accuracy, in particular how closely the recommender’s predicted ratings are to the users’ true ratings. However, it has been recognized that other recommendation qualities—such as whether the list of recommendations is diverse and whether it contains novel items—may have a significant impact on the overall quality of a recommender system. Consequently, in recent years, the focus of recommender systems research has shifted to include a wider range of “beyond accuracy” objectives.In this article, we present a survey of the most discussed beyond-accuracy objectives in recommender systems research: diversity, serendipity, novelty, and coverage. We review the definitions of these objectives and corresponding metrics found in the literature. We also review works that propose optimization strategies for these beyond-accuracy objectives. Since the majority of works focus on one specific objective, we find that it is not clear how the different objectives relate to each other.Hence, we conduct a set of offline experiments aimed at comparing the performance of different optimization approaches with a view to seeing how they affect objectives other than the ones they are optimizing. We use a set of state-of-the-art recommendation algorithms optimized for recall along with a number of reranking strategies for optimizing the diversity, novelty, and serendipity of the generated recommendations. For each reranking strategy, we measure the effects on the other beyond-accuracy objectives and demonstrate important insights into the correlations between the discussed objectives. For instance, we find that rating-based diversity is positively correlated with novelty, and we demonstrate the positive influence of novelty on recommendation coverage.

Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems

More and more people read the news online, e.g., by visiting the websites of their favorite newspapers or by navigating the sites of news aggregators. However, the abundance of news information that is published online every day through different channels can make it challenging for readers to locate the content they are interested in. The goal of News Recommender Systems (NRS) is to make reading suggestions to users in a personalized way. Due to their practical relevance, a variety of technical approaches to build such systems have been proposed over the last two decades. In this work, we review the state-of-the-art of designing and evaluating news recommender systems over the last ten years. One main goal of the work is to analyze which particular challenges of news recommendation (e.g., short item life times and recency aspects) have been well explored and which areas still require more work. Furthermore, in contrast to previous surveys, the paper specifically discusses methodological questions and today’s academic practice of evaluating and comparing different algorithmic news recommendation approaches based on accuracy measures.

News recommender systems – Survey and roads ahead

We summarize most efforts on serendipity in recommender systems.We compare definitions of serendipity in recommender systems.We classify the state-of-the-art serendipity-oriented recommendation algorithms.We review methods to assess serendipity in recommender systems.We provide the future directions of serendipity in recommender systems. Recommender systems use past behaviors of users to suggest items. Most tend to offer items similar to the items that a target user has indicated as interesting. As a result, users become bored with obvious suggestions that they might have already discovered. To improve user satisfaction, recommender systems should offer serendipitous suggestions: items not only relevant and novel to the target user, but also significantly different from the items that the user has rated. However, the concept of serendipity is very subjective and serendipitous encounters are very rare in real-world scenarios, which makes serendipitous recommendations extremely difficult to study. To date, various definitions and evaluation metrics to measure serendipity have been proposed, and there is no wide consensus on which definition and evaluation metric to use. In this paper, we summarize most important approaches to serendipity in recommender systems, compare different definitions and formalizations of the concept, discuss serendipity-oriented recommendation algorithms and evaluation strategies to assess the algorithms, and provide future research directions based on the reviewed literature.

A survey of serendipity in recommender systems

Most recommender algorithms produce types similar to those the active user has accessed before. This is because they measure user similarity only from the co-rating behaviors against items and compute recommendations by analyzing the items possessed by the users most similar to the active user. In this paper, we define item novelty as the smallest distance from the class the user accessed before to the class that includes target items over the taxonomy. Then, we try to accurately recommend highly novel items to the user. First, our method measures user similarity by employing items rated by users and a taxonomy of items. It can accurately identify many items that may suit the user. Second, it creates a graph whose nodes are users; weighted edges are set between users according to their similarity. It analyzes the user graph and extracts users that are related on the graph though the similarity between the active user and each of those users is not high. The users so extracted are likely to have highly novel items for the active user. An evaluation conducted on several datasets finds that our method accurately identifies items with higher novelty than previous methods.

Classical music for rock fans?: novel recommendations for expanding user interests

In this paper, we evaluate the effectiveness of a semantic smoothing technique to organize folksonomy tags. Folksonomy tags have no explicit relations and vary because they form uncontrolled vocabulary. We discriminates so-called subjective tags like "cool" and "fun" from folksonomy tags without any extra knowledge other than folksonomy triples and use the level of tag generalization to form the objective tags into a hierarchy. We verify that entropy of folksonomy tags is an effective measure for discriminating subjective folksonomy tags. Our hierarchical tag allocation method guarantees the number of children nodes and increases the number of available paths to a target node compared to an existing tree allocation method for folksonomy tags.

The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags

Tracking user interests over time is important for making accurate recommendations. However, the widely-used time-decay-based approach worsens the sparsity problem because it deemphasizes old item transactions. We introduce two ideas to solve the sparsity problem. First, we divide the users' transactions into epochs i.e. time periods, and identify epochs that are dominated by interests similar to the current interests of the active user. Thus, it can eliminate dissimilar transactions while making use of similar transactions that exist in prior epochs. Second, we use a taxonomy of items to model user item transactions in each epoch. This well captures the interests of users in each epoch even if there are few transactions. It suits the situations in which the items transacted by users dynamically change over time; the semantics behind classes do not change so often while individual items often appear and disappear. Fortunately, many taxonomies are now available on the web because of the spread of the Linked Open Data vision. We can now use those to understand dynamic user interests semantically. We evaluate our method using a dataset, a music listening history, extracted from users' tweets and one containing a restaurant visit history gathered from a gourmet guide site. The results show that our method predicts user interests much more accurately than the previous time-decay-based method.

/pdf/collaborative-filtering-by-analyzing-dynamic-user-interests-9y01sy0dr4.pdf

Collaborative filtering by analyzing dynamic user interests modeled by taxonomy

This paper proposes a method for ranking entities (e.g. products, people, etc.) that uses the comparative sentences described in text such as reviews, blogs, etc. as an indicator of an individual entity's value. A comparative sentence expresses a relation between two entities. The comparative sentence "The quality of A is better than that of B" is expressed by the comparative relation {A,B,quality,better}. Given a query (set of queries), the proposed method automatically finds the competitive entities and extracts the comparative relations among them. From the vast amount of comparative relations so extracted, the proposed method then generates a graph modeling the behavior of a "potential customer" to assess the relative importance of every entity against its competitors. These ranking results help potential customers to know the position of the entity among related entities and decide which one to choose.

Ranking Entities Using Comparative Relations

In order to create more attractive tagclouds that get people interested in tagged content, we propose a simple but novel tagcloud where font size is determined by tag's entropy value, not the popularity to its content. Our method raises users' emotional interest in the content by emphasizing more emotional tags. Our initial experiments show that emotional tagclouds attract more attention than normal tagclouds at first look; thus they will enhance the role of tagcloud as a social signaller.

/pdf/signaling-emotion-in-tagclouds-j1ui9c9in3.pdf

Toshio Uchiyama

Papers

Classical music for rock fans?: novel recommendations for expanding user interests

The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags

Collaborative filtering by analyzing dynamic user interests modeled by taxonomy

Ranking Entities Using Comparative Relations

Signaling emotion in tagclouds