Tag genome is widely used in recommender systems research to, for example, measure item similarity, make recommendations and generate recommendation explanations. Applying tag genome to problems in cross-domain recommendation, however, is complicated by the limited item overlap between cross-domain recommendation data sets and the available tag genomes. Furthermore, existing tag prediction models rely on content-based features that are not readily available in a majority of recommendation data sets. To address these issues, we generated tag genomes for both movies and books based on the Amazon data set, which is widely used in cross-domain recommendation research. These new tag genomes are over 200 × larger than the previous versions and can support comparative evaluation of tag-based and collaborative methods, facilitate the development of new cross-domain recommendation algorithms and provide a foundation for studying phenomena, such as serendipity and diversity, across multiple domains. Both data sets and the data generation pipeline are freely available at https://github.com/Bionic1251/Expanded-Tag-Genomes.

We present Paths and Recreation, an interactive recommender system that integrates weather data and users’ current locations with a municipal service map to recommend nearby points-of-interest related to physical activities, such as outdoor gyms and swimming pools. The system can also generate and recommend routes for walking and cycling through your neighbourhood. Our primary goal was to make the recommendation of physical activities as inclusive as possible. We, therefore, included health-related filters to help users select activities at an appropriate level of intensity, and accessibility filters to assist, for example, wheelchair users, those with reduced mobility and people pushing strollers. We conducted a user study (N=16) to compare our system to common mobile apps (i.e., weather app, maps and web browser). In the study, participants needed to identify appropriate nearby physical activities by taking various health and accessibility requirements into consideration. Participants highlighted our system’s ease of use and found the integration of weather and health conditions to be useful for decision-making.

Serendipity has been recognized as a valuable property of recommender systems. While there is a lack of consensus on the precise definition of serendipity, it is often conceptualized in terms of the relevance, novelty and unexpectedness of recommendations. However, the common understanding and original meaning of serendipity is conceptually broader, requiring serendipitous encounters to be neither novel nor unexpected. Recent work has highlighted the various ways in which serendipity can manifest, leading to a more generalized definition of serendipity. In this paper, we conducted an observational study where we collected 2002 survey responses from 397 users of an online article recommender system. In our study, we found a significant proportion of serendipitous recommendations were missed by the conventional definitions used in the recommender systems research literature, exposing the “dark matter” of serendipity that has been overlooked in prior studies. Interestingly, users’ opinions of which articles should be considered serendipitous did not strongly align with any of the definitions investigated. Furthermore, despite several user behaviors being significantly associated with a majority of definitions of serendipity, the overall goodness of fit was very low. Our findings highlight the issues of evaluating serendipity in recommender systems and the challenge of reconciling serendipity with user expectations.

Recommender systems typically operate within a single domain, for example, recommending books based on users' reading habits. If such data is unavailable, it may be possible to make cross-domain recommendations and recommend books based on user preferences from another domain, such as movies. However, despite considerable research on cross-domain recommendations, no studies have investigated their impact on users' behavioural intentions or system perceptions compared to single-domain recommendations. Similarly, while single-domain explanations have been shown to improve users' perceptions of recommendations, there are no comparable studies for the cross-domain case.

Recommender systems suggest items, such as movies or books, to users based on their interests. These systems often suggest items that users are either already familiar with or could easily have found on their own without additional assistance. To overcome these problems, recommender systems aim to suggest serendipitous items. While there is a lack of consensus in the recommender systems research community on the definition of serendipity, it is often conceptualized as a complex combination of relevance, novelty and unexpectedness. However, the common understanding and original meaning of serendipity is conceptually broader, requiring serendipitous encounters to be neither novel nor unexpected. Recent work in the social sciences has highlighted the various ways that serendipity can manifest, leading to a more generalized definition of serendipity. We argue that the study of serendipity in recommender systems would benefit from considering items that are serendipitous under this more general definition, giving us a deeper understanding of the item characteristics and behavioral impact of serendipitous recommendations. These findings will help us to better optimize recommender systems for serendipity. In this paper, we explore various definitions of serendipity and propose a novel formalization of what it means for recommendations to be serendipitous. Lastly, we present an experimental design for how serendipity can be measured in a deployed recommender system.

Attaching tags to items, such as books or movies, is found in many online systems. While a majority of these systems use binary tags, continuous item-tag relevance scores, such as those in tag genome, offer richer descriptions of item content. For example, tag genome for movies assigns the tag “gangster” to the movie “The Godfather (1972)” with a score of 0.93 on a scale of 0 to 1. Tag genome has received considerable attention in recommender systems research and has been used in a wide variety of studies, from investigating the effects of recommender systems on users to generating ideas for movies that appeal to certain user groups.

Traditionally, recommender systems provide a list of suggestions to a user based on past interactions with items of this user. These recommendations are usually based on user preferences for items and generated with a delay. Critiquing recommender systems allow users to provide immediate feedback to recommendations with tags and receive a new set of recommendations in response. However, these systems often require rich item descriptions that contain relevance scores indicating the strength, with which a tag applies to an item. For example, this relevance score could indicate how violent the movie "The Godfather" is on a scale from 0 to 1. Retrieving these data is a very demanding process, as it requires users to explicitly indicate the degree to which a tag applies to an item. This process can be improved with machine learning methods that predict tag relevance. In this paper, we explore the dataset from a different study, where the authors collected relevance scores on movie-tag pairs. In particular, we define the tag relevance prediction problem, explore the inconsistency of relevance scores provided by users as a challenge of this problem and present a method, which outperforms the state-of-the-art method for predicting tag relevance. We found a moderate inconsistency of user relevance scores. We also found that users tend to disagree more on subjective tags, such as "good acting", "bad plot" or "quotable" than on objective tags, such as "animation", "cars" or "wedding", but the disagreement of users regarding objective tags is also moderate.

Related item recommendations have a long history in recommender systems, but they tend to be a static list of similar items with respect to a target item of interest without any support of user control. In this paper, we propose ClusterExplorer, a novel approach for enabling user control over related recommendations. The approach allows users to explore the latent space of user-item interactions through controlling related recommendations. We evaluated ClusterExplorer in the book domain with 42 participants recruited in a public library and found that our approach has higher user satisfaction of browsing items and is more helpful in finding interesting items compared to traditional related item recommendations.

Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, but generating serendipitous recommendations is difficult due to the lack of training data. To the best of our knowledge, there are many large datasets containing relevance scores (relevance oriented) and only one publicly available dataset containing a relatively small number of serendipity scores (serendipity oriented). This limits the learning capabilities of serendipity oriented algorithms. Therefore, in the absence of any known deep learning algorithms for recommending serendipitous items and the lack of large serendipity oriented datasets, we introduce SerRec our novel transfer learning method to recommend serendipitous items. SerRec uses transfer learning to firstly train a deep neural network for relevance scores using a large dataset and then tunes it for serendipity scores using a smaller dataset. Our method shows benefits of transfer learning for recommending serendipitous items as well as performance gains over the state-of-the-art serendipity oriented algorithms