Moodplay

Research in recommender systems has traditionally focused on improving the predictive accuracy of recommendations by developing new algorithms or by incorporating new sources of data. However, several studies have shown that accuracy does not always correlate with a better user experience, leading to recent research that puts emphasis on Human-Computer Interaction in order to investigate aspects of the interface and user characteristics that influence the user experience on recommender systems. Following this new research this paper presents SetFusion, a visual user-controllable interface for hybrid recommender system. Our approach enables users to manually fuse and control the importance of recommender strategies and to inspect the fusion results using an interactive Venn diagram visualization. We analyze the results of two field studies in the context of a conference talk recommendation system, performed to investigate the effect of user controllability in a hybrid recommender. Behavioral analysis and subjective evaluation indicate that the proposed controllable interface had a positive effect on the user experience.

There have been significant advances made in the research of recommender systems over the past decades and have been implemented in both industry and academia. Recently, multi-criteria ratings are being incorporated into traditional recommender systems to further improve their quality, especially to handle the data sparsity and cold start issues. However, incorporation of multi-criteria ratings have improved the performance of the recommendation, but at the same time, multi-dimensionality issue also arises. This paper presents a clustering-based recommendation approach which is used for dealing with the multi-dimensionality issue in multi-criteria recommender systems. Here, we cluster the users based on their individual criteria ratings using K-means clustering and the intra-cluster similarity is computed using Mahalanobis distance measure for neighbourhood set generation. This improves the recommendations quality and predictive accuracy of both traditional and clusteringbased collaborative recommendations. The Yahoo! Movies dataset was used for testing the approach and the experiment conducted shows promising results.

Over the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-addressed question. According to the most common definition, serendipity consists of three components: relevance, novelty and unexpectedness, where each component has multiple variations. In this paper, we looked at eight different definitions of serendipity and asked users how they perceived them in the context of movie recommendations. We surveyed 475 users of the movie recommender system, MovieLens regarding 2146 movies in total and compared those definitions of serendipity based on user responses. We found that most kinds of serendipity and all the variations of serendipity components broaden user preferences, but one variation of unexpectedness hurts user satisfaction. We found effective features for detecting serendipitous movies according to definitions that do not include this variation of unexpectedness. We also found that different variations of unexpectedness and different kinds of serendipity have different effects on preference broadening and user satisfaction. Among movies users rate in our system, up to 8.5% are serendipitous according to at least one definition of serendipity, while among recommendations that users receive and follow in our system, this ratio is up to 69%.

Predicting process behavior meets factorization machines

Tag and item recommendation are both important tasks in recommender systems. Item recommendation, learning user-item rating patterns, helps users to discover a list of items that are more likely to match their interests. On the other hand, tag recommendation, exploiting relationships of user-tag-item, assists users to find a personalized list of tags that can precisely describe their experience on items. Existing methods often consider these two tasks independently. However, these two tasks are inherently related because they both use information from same groups of users and items. In other words, it is the same users who give ratings and annotate tags to the same set of items. Tags may explain why a user purchase an item. For instance, customers on Yelp not only give a five star rating for a restaurant but also leave comments/tags to express their positive sentiments. Users with similar interests might give similar ratings and post similar tags to certain items. The co-occurrence of users, items and tags motivates us to jointly perform item and tag recommendation to better model users' behavior by leveraging their personalized items and tags. In this work, we propose a cross-domain learning method that seamlessly integrates tag and item recommendation into one unified framework. In particular, we utilize the knowledge transferred between two tasks to collaboratively rank items and tags for users through coupled tensor-matrix factorization. We also provide a principled way to incorporate additional auxiliary information about users (e.g., users' relationships in a social network) and items (e.g., descriptions of items in Wikipedia) to improve recommendation accuracy for both tasks. Empirical results demonstrate the effectiveness of our proposed model.

This paper shows a method for building and publishing datasets in commercial services. Datasets contribute to the development of research in machine learning and recommender systems. In particular, because recommender systems play a central role in many commercial services, publishing datasets from the services are in great demand from the recommender system community. However, the publication of datasets by commercial services may have some business risks to those companies. To publish a dataset, this must be approved by a business manager of the service. Because many business managers are not specialists in machine learning or recommender systems, the researchers are responsible for explaining to them the risks and benefits. We first summarize three challenges in building datasets from commercial services: (1) anonymize the business metrics, (2) maintain fairness, and (3) reduce the popularity bias. Then, we formulate the problem of building and publishing datasets as an optimization problem that seeks the sampling weight of users, where the challenges are encoded as appropriate loss functions. We applied our method to build datasets from the raw data of our real-world mobile news delivery service. The raw data has more than 1,000,000 users with 100,000,000 interactions. Each dataset was built in less than 10 minutes. We discussed the properties of our method by checking the statistics of the datasets and the performances of typical recommender system algorithms.

<h2>Abstract</h2> Recent research in recommender systems has demonstrated the advantages of pairwise ranking in recommendation. In this work, we focus on the state-of-the-art pairwise ranking loss function, Bayesian Personalized Ranking (BPR), and aim to address two of its limitations, namely: (1) the lack of explainability and (2) exposure bias. We propose a recommendation framework that encompasses various loss functions that are based on BPR and which aim to mitigate the aforementioned limitations. Our open-source framework includes code to train and tune state-of-the-art pairwise ranking recommender systems on benchmark datasets and evaluate them based on the three criteria of ranking accuracy, explainability, and popularity debiasing.

With the advent of the era of big data, in order to actively cope with the massive and unstructured "information overload", recommendation systems are widely used in network products and have a profound impact on people's lives. At present, the research of recommendation system has been developed for more than ten years, but many of them are still in the incomplete stage, there are problems such as sparse data, insufficient mining of user implicit preferences, and inaccurate semantic description of item features. These pictures, videos and other content are full of users' emotions, which contain a lot of user emotions, which need further Excavation and application, resulting in huge social and economic benefits. However, there are few researches on sentiment analysis of this kind of information, and the multi-modal sentiment analysis of text, images and other information is particularly weak. Multimodal information has the same emotional characteristics and can describe the user's emotional changes from different angles, so as to obtain more accurate emotional identification.

Causality is gaining more and more attention in the machine learning community and consequently also in recommender systems research. The limitations of learning offline from observed data are widely recognized, however, applying debiasing strategies like Inverse Propensity Weighting does not always solve the problem of making wrong estimates. This concept paper contributes a summary of debiasing strategies in recommender systems and the design of several toy examples demonstrating the limits of these commonly applied approaches. Therefore, we propose to map the causality frameworks of potential outcomes and structural causal models onto the recommender systems domain in order to foster future research and development. For instance, applying causal discovery strategies on offline data to learn the causal graph in order to compute counterfactuals or improve debiasing strategies.

Sedentary lifestyles and bad eating habits influence the onset of many serious health problems. Healthy behavior change is an arduous task, and requires a careful planning. In this thesis, we propose that behavior recommenders can help their users achieve healthy behavior change. Such a system should inspire its users with small, incremental and achievable goals. For this, it must resolve a trade-off between two opposing objectives: help the user achieve a steady improvement in target behavior, and avoid extreme goals that may injure or discourage the user. This is an unprecedented challenge in the recommender systems research. If the system understands the impacts of past interventions for behavior change, it can determine its usersâ behavioral responses to its own recommendations. This implies a specific data curation, in which we not only measure people's behavior but also deliberately introduce an intervention to monitor its effect on people's patterns. In turn, the system can use these existing users' information to derive the right procedure for effective recommendations. In this study we capitalize on this insight and develop InspiRE - our behavior recommender framework. Through InspiRE we propose the following contributions: 1) We design the data curation. 2) We develop the novel approaches for behavior profiling 3) We develop an evaluation process for this novel type of recommender system, and also compare it with traditional, similarity-based recommendation approach. We curate a dataset that contains information of daily step counts and social intervention for 83 people. InspiRE successfully uses the observations from this dataset, and proposes recommendations that are both effective and feasible. We also show that InspiRE can generalize to other dimensions of well being: we demonstrate this through a dataset that contains the snacking patterns of 73 people, who receive message-based interventions. We observe that InspiRE's recommendation strategy is in line with theories of behavior change.

Since the advent of Recommender Systems (RSs), many papers have been published with the majority focusing on creating more accurate algorithms. The more accurate the algorithm is, the better the recommendation is predicted to be for users. Recently, RSs researchers pointed out that the embedding of the recommendation methods in the User Experience (UX) dramatically affects the recommender systems’ value to users. This paper proposes a framework to explore how contexts influence UX with Scholarly recommender systems and identifies relevant contexts to be incorporated in the UX. We first review existing models and theories of UX that are most applicable to RSs and identify gaps in existing work. The framework clarifies how contexts can influence UX with SRSs and enriches our conceptual understanding of how contextual information influences UX of scholarly recommender systems. It can serve as a foundation for further theoretical and empirical investigation. An experiment evaluating the user experience is performed using the quantitative method of Partial Least Squares (PLS) Regression and Structural Equation Modeling (SEM) to examine the developed conceptual framework.

Collaborative filtering techniques remain a staple in recommender systems research and applications. With the plethora of research done in recommender systems, some more recent works applied deep learning with great success. We stack a deep neural network recommender onto a shallow one for item recommendations in this work. Our experiments with popular datasets indicate that the proposed technique outperforms other state-of-the art techniques and baselines in key performance metrics.

Recommender system exists everywhere in the business world. From Goodreads to TikTok, customers of internet products become more addicted to the products thanks to the technology. Industrial practitioners focus on increasing the technical accuracy of recommender systems while at same time balancing other factors such as diversity and serendipity. In spite of the length of the research and development history of recommender systems, there has been little discussion on how to take advantage of visualization techniques to facilitate the algorithmic design of the technology. In this paper, we use a series of data analysis and visualization techniques such as Takens Embedding, Determinantal Point Process and Social Network Analysis to help people develop effective recommender systems by predicting intermediate computational cost and output performance. Our work is pioneering in the field, as to our limited knowledge, there have been few publications (if any) on visualization of recommender systems.

ABSTRACTCollaborative filtering is a widely used method in recommender systems research. However, contrary to the assumption that it relies solely on rating data, many contemporary models incorporate review information to address issues such as data sparsity. Although previous recommender systems utilised review texts to capture user preferences and item features, they often rely on a single‐embedding model to represent these features, which may limit the richness of the extracted information. Recent advancements suggest that combining multiple pre‐trained embedding models can enhance text representation by leveraging the strengths of different encoding methods. In this study, we propose a novel recommender system model, the Multi‐embedding Fusion Network for Recommendation (MFNR), which employs a multi‐embedding approach to effectively capture and represent user and item features in review texts. Specifically, the proposed model integrates Bidirectional Encoder Representations from Transformers (BERT) and its optimised variant, RoBERTa, both of which are pre‐trained transformer‐based models designed for natural language understanding. By leveraging their contextual embeddings, our model extracts enriched feature representations from review texts. Extensive experiments conducted on real‐world review datasets from Amazon.com and Goodreads.com demonstrate that MFNR significantly outperforms existing baseline models, achieving an average improvement of 9.18% in RMSE and 14.81% in MAE. These results highlight the efficacy of the multi‐embedding approach, indicating its potential for broader application in complex recommendation scenarios.

Recommender systems research is being slowed by the difficulty of replicating and comparing research results. Published research uses various experimental methodologies and metrics that are difficult to compare. It also often fails to sufficiently document the details of proposed algorithms or the evaluations employed. Researchers waste time reimplementing well-known algorithms, and the new implementations may miss key details from the original algorithm or its subsequent refinements. When proposing new algorithms, researchers should compare them against finely-tuned implementations of the leading prior algorithms using state-of-the-art evaluation methodologies. With few exceptions, published algorithmic improvements in our field should be accompanied by working code in a standard framework, including test harnesses to reproduce the described results. To that end, we present the design and freely distributable source code of LensKit, a flexible platform for reproducible recommender systems research. LensKit provides carefully tuned implementations of the leading collaborative filtering algorithms, APIs for common recommender system use cases, and an evaluation framework for performing reproducible offline evaluations of algorithms. We demonstrate the utility of LensKit by replicating and extending a set of prior comparative studies of recommender algorithms --- showing limitations in some of the original results --- and by investigating a question recently raised by a leader in the recommender systems community on problems with error-based prediction evaluation.