Optimizing personalized ranking in recommender systems with metadata awareness

In this paper, we propose an item recommendation algorithm based on latent factors which uses implicit feedback from users to optimize the ranking of items according to individual preferences. The novelty of the algorithm is the integration of content metadata to improve the quality of recommendations. Such descriptions are an important source to construct a personalized set of items which are meaningfully related to the user’s main interests. The method is evaluated on two diferente datasets, being compared against another approach reported in the literature. The results demonstrate the effectiveness of supporting personalized ranking with metadata awareness.CAPESCNPqFAPESP (grant #2013/22547-1 and #2012/13830-9

PERSONALIZED RANKING OF MOVIES: EVALUATING DIFFERENT METADATA TYPES AND RECOMMENDATION STRATEGIES

This paper proposes a study and comparison among a variety of metadata types in order to identify the most relevant pieces of information in order to identify the most relevant pieces of information in personalized ranking of movie items. We used four algorithms available in the literature to analyze the descriptions, and compared each other using the metadata extracted from two datasets, namely MovieLens and IMDB. As a result of our evaluation, we found out that the movies' genres and actors are the kind of description that generates better predictions for the considered content-based recommenders.This paper proposes a study and comparison among a variety of metadata types in order to identify the most relevant pieces of information in order to identify the most relevant pieces of information in personalized ranking of movie items. We used four algorithms available in the literature to analyze the descriptions, and compared each other using the metadata extracted from two datasets, namely MovieLens and IMDB. As a result of our evaluation, we found out that the movies' genres and actors are the kind of description that generates better predictions for the considered content-based recommenders

A flexible framework for evaluating user and item fairness in recommender systems

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11257-020-09285-1One common characteristic of research works focused on fairness evaluation (in machine learning) is that they call for some form of parity (equality) either in treatment—meaning they ignore the information about users’ memberships in protected classes during training—or in impact—by enforcing proportional beneficial outcomes to users in different protected classes. In the recommender systems community, fairness has been studied with respect to both users’ and items’ memberships in protected classes defined by some sensitive attributes (e.g., gender or race for users, revenue in a multi-stakeholder setting for items). Again here, the concept has been commonly interpreted as some form of equality—i.e., the degree to which the system is meeting the information needs of all its users in an equal sense. In this work, we propose a probabilistic framework based on generalized cross entropy (GCE) to measure fairness of a given recommendation model. The framework comes with a suite of advantages: first, it allows the system designer to define and measure fairness for both users and items and can be applied to any classification task; second, it can incorporate various notions of fairness as it does not rely on specific and predefined probability distributions and they can be defined at design time; finally, in its design it uses a gain factor, which can be flexibly defined to contemplate different accuracy-related metrics to measure fairness upon decision-support metrics (e.g., precision, recall) or rank-based measures (e.g., NDCG, MAP). An experimental evaluation on four real-world datasets shows the nuances captured by our proposed metric regarding fairness on different user and item attributes, where nearest-neighbor recommenders tend to obtain good results under equality constraints. We observed that when the users are clustered based on both their interaction with the system and other sensitive attributes, such as age or gender, algorithms with similar performance values get different behaviors with respect to user fairness due to the different way they process data for each user clusterThe authors thank the reviewers for their thoughtful comments and suggestions. This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades (Reference: 123496 Y. Deldjoo et al. PID2019-108965GB-I00) and in part by the Center for Intelligent Information Retrieval. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor

Improving personalized ranking in recommender systems with topic hierarchies and implicit feedback

The knowledge of semantic information about the content and user’s preferences is an important issue to improve recommender systems. However, the extraction of such meaningful metadata needs an intense and time-consuming human effort, which is impractical specially with large databases. In this paper, we mitigate this problem by proposing a recommendation model based on latent factors and implicit feedback which uses an unsupervised topic hierarchy constructor algorithm to organize and collect metadata at different granularities from unstructured textual content. We provide an empirical evaluation using a dataset of web pages written in Portuguese language, and the results show that personalized ranking with better quality can be generated using the extracted topics at medium granularity.CAPESCNPqFAPESP (grant #2013/25547-1 and #2012/13830-9

Combining multiple metadata types in movies recommendation using ensemble algorithms

In this paper, we analyze the application of ensemble algorithms to improve the ranking recommendation problem with multiple metadata. We propose three generic ensemble strategies that do not require modification of the recommender algorithm. They combine predictions from a recommender trained with distinct metadata into a unified rank of recommended items. The proposed strategies are Most Pleasure, Best of All and Genetic Algorithm Weighting. The evaluation using the HetRec 2011 MovieLens 2k dataset with five different metadata (genres, tags, directors, actors and countries) shows that our proposed ensemble algorithms achieve a considerable 7% improvement in the Mean Average\ud Precision even with state-of-art collaborative filtering algorithms

Presenting tiered recommendations in social activity streams

Modern social networking sites offer node-centralized streams that display recent updates from the other nodes in one's network. While such social activity streams are convenient features that help alleviate information overload, they can often become overwhelming themselves, especially high-throughput streams like Twitter’s home timelines. In these cases, recommender systems can help guide users toward the content they will find most important or interesting. However, current efforts to manipulate social activity streams involve hiding updates predicted to be less engaging or reordering them to place new or more engaging content first. These modifications can lead to decreased trust in the system and an inability to consume each update in its chronological context. Instead, I propose a three-tiered approach to displaying recommendations in social activity streams that hides nothing and preserves original context by highlighting updates predicted to be most important and de-emphasizing updates predicted to be least important. This presentation design allows users easily to consume different levels of recommended items chronologically, is able to persuade users to agree with its positive recommendations more than 25% more often than the baseline, and shows no significant loss of perceived accuracy or trust when compared with a filtered stream, possibly even performing better when extreme recommendation errors are intentionally introduced. Numerous directions for future research follow from this work that can shed light on how users react to different recommendation presentation designs and explain how study of an emphasis-based approach might help improve the state of the art