arXiv subjects

Samira Vaez Barenji

Publications and source records attributed to Samira Vaez Barenji.

4 recordsLinked to original sources

arXiv2026

Book Readership During Movie Releases: An Exploratory Analysis

Exogenous events can temporarily change the relevance of items in recommender systems, but these shifts are often not visible in historical interaction data until after users have already responded. In book recommendation, movie adaptations provide a clear example of such events: the release of a movie based on a book can temporarily increase attention to the source text and change its relevance for some readers. We examine this phenomenon using a large Goodreads dataset matched to movie release dates. We find a clear spike in readership around the release month, and then we evaluate existing recommendation models to understand how they rank movie-adapted books around the movie release date.

cs.IR↗

arXiv2026

On the Convergent Validity of Offline Evaluation Designs for Recommender Systems

Offline evaluation on historical interaction logs is the most common evaluation methodology for recommender systems. However, such evaluations depend on sparse, incomplete, or biased data, which raises concerns about whether commonly used evaluation setups reliably reflect true user preferences. In this work, we study how offline evaluation design choices affect the validity of recommender system comparisons. We evaluate a set of recommendation models across several evaluation setups that vary key factors such as data filtering thresholds and candidate set construction. To assess the validity of these configurations, we measure the correlation between model rankings obtained from conventional train-test splits on sparse interaction data and rankings from evaluations based on dense ground-truth user feedback. We use this agreement as an indication of their validity with respect to true user preferences. Our results show that the validity of sparse evaluation depends on the dataset and the specific dense evaluation targets, and that there is no uniformly best offline evaluation design.

cs.IR↗

arXiv2025

User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation

Data is an essential resource for studying recommender systems. While there has been significant work on improving and evaluating state-of-the-art models and measuring various properties of recommender system outputs, less attention has been given to the data itself, particularly how data has changed over time. Such documentation and analysis provide guidance and context for designing and evaluating recommender systems, particularly for evaluation designs making use of time (e.g., temporal splitting). In this paper, we present a temporal explanatory analysis of the UCSD Book Graph dataset scraped from Goodreads, a social reading and recommendation platform active since 2006. We measure the book interaction data using a set of activity, diversity, and fairness metrics; we then train a set of collaborative filtering algorithms on rolling training windows to observe how the same measures evolve over time in the recommendations. Additionally, we explore whether the introduction of algorithmic recommendations in 2011 was followed by observable changes in user or recommender system behavior.

cs.IR↗

arXiv2023

Managing multi-facet bias in collaborative filtering recommender systems

Due to the extensive growth of information available online, recommender systems play a more significant role in serving people's interests. Traditional recommender systems mostly use an accuracy-focused approach to produce recommendations. Today's research suggests that this single-dimension approach can lead the system to be biased against a series of items with certain attributes. Biased recommendations across groups of items can endanger the interests of item providers along with causing user dissatisfaction with the system. This study aims to manage a new type of intersectional bias regarding the geographical origin and popularity of items in the output of state-of-the-art collaborative filtering recommender algorithms. We introduce an algorithm called MFAIR, a multi-facet post-processing bias mitigation algorithm to alleviate these biases. Extensive experiments on two real-world datasets of movies and books, enriched with the items' continents of production, show that the proposed algorithm strikes a reasonable balance between accuracy and both types of the mentioned biases. According to the results, our proposed approach outperforms a well-known competitor with no or only a slight loss of efficiency.

cs.IR↗