From implicit preferences to ratings: Video games recommendation based on collaborative filtering

This work studies and compares the performance of collaborative filtering algorithms, with the intent of proposing a videogame-oriented recommendation system. This system uses information from the video game platform Steam, which contains information about the game usage, corresponding to the implicit feedback that was later transformed into explicit feedback. These algorithms were implemented using the Surprise library, that allows to create and evaluate recommender systems that deal with explicit data. The algorithms are evaluated and compared with each other using metrics such as RSME, MAE, Precision@k, Recall@k and F1@k. We have concluded that computationally low demanding approaches can still obtain suitable results.info:eu-repo/semantics/acceptedVersio

Recommender systems for players of online video games

The content in this project is the approach, exploration, analysis and use of recommender systems to integrate an implementation of one system that learns the players’ behavior and recommends them to other players, to show recommender systems as a way of enhancing the player experience

A novel evaluation framework for recommender systems in big data environments

Henriques, R., & Pinto, L. (2023). A novel evaluation framework for recommender systems in big data environments. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2023.120659---We gratefully acknowledge the support of Aptoide in providing access to the data which made this project possible. This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.Recommender systems were first introduced to solve information overload problems in enterprises. Over the last few decades, recommender systems have found applications in several major websites related to e-commerce, music and video streaming, travel and movie sites, social media, and mobile app stores. Several methods have been proposed over the years to build recommender systems. However, very little work has been done in recommender system evaluation metrics. The most common approach to measuring recommender system’s performance in offline settings is to employ micro or macro averaged versions of standard machine-learning measures. Profit or other business-oriented metrics have been proposed for other predictive analytics problems, such as churn prediction. However, no such metrics have emerged for the recommender system context. In this work, we propose a novel evaluation metric that incorporates information from the online-platform userbase’s behavior. This metric’s rationale is that the recommender system ought to improve customers’ repeatead use of an online platform beyond the baseline level (i.e. in the absence of a recommender system). An empirical application of this novel metric is also presented in a real-world mobile app store, which integrates the dynamics of large-scale big data environments, which are common deployment scenarios for these types of recommender systems. The resulting profit metric is shown to correlate with the existing metrics while also being capable of integrating cost information, thereby providing an additional business benefit context, which allows us to differentiate between two similarly performing models.publishersversionepub_ahead_of_prin

RecExplainer: Aligning Large Language Models for Recommendation Model Interpretability

Recommender systems are widely used in various online services, with embedding-based models being particularly popular due to their expressiveness in representing complex signals. However, these models often lack interpretability, making them less reliable and transparent for both users and developers. With the emergence of large language models (LLMs), we find that their capabilities in language expression, knowledge-aware reasoning, and instruction following are exceptionally powerful. Based on this, we propose a new model interpretation approach for recommender systems, by using LLMs as surrogate models and learn to mimic and comprehend target recommender models. Specifically, we introduce three alignment methods: behavior alignment, intention alignment, and hybrid alignment. Behavior alignment operates in the language space, representing user preferences and item information as text to learn the recommendation model's behavior; intention alignment works in the latent space of the recommendation model, using user and item representations to understand the model's behavior; hybrid alignment combines both language and latent spaces for alignment training. To demonstrate the effectiveness of our methods, we conduct evaluation from two perspectives: alignment effect, and explanation generation ability on three public datasets. Experimental results indicate that our approach effectively enables LLMs to comprehend the patterns of recommendation models and generate highly credible recommendation explanations.Comment: 12 pages, 8 figures, 4 table

Deep Learning Techniques on Recommender Systems

Ένα σύστημα συστάσεων είναι ένα εργαλείο που φιλτράρει πληροφορίες και προτείνει στους χρήστες περιεχόμενο που σχετίζεται με τα ενδιαφέροντά τους. Έχει παρατηρηθεί αύξηση στην χρήση των συστημάτων συστάσεων τα τελευταία χρόνια ως αποτέλεσμα της αυξανόμενης χρήσης του διαδικτύου η οποία παρέχει στους ερευνητές τεράστιες πο­ σότητες δεδομένων για τους χρήστες. Ο σκοπός αυτής της πτυχιακής εργασίας είναι να μελετήσει τις διάφορες τεχνικές που εφαρμόζονται στα συστήματα συστάσεων καθώς και τα μοντέλα βαθιάς μάθησης που χρησιμοποιούνται για την ενίσχυση αυτών των συστη­ μάτων. Επιπλέον, οι μέθοδοι αξιολόγησης των συστημάτων συστάσεων περιγράφονται μαζί με τις προκλήσεις που αυτά αντιμετωπίζουν. Στην συνέχεια αυτής της μελέτης, περι­ γράφεται η υλοποίηση ενός συστήματος συστάσεων για βιντεοπαιχνίδια που χρησιμοποιεί αλγόριθμους βαθιάς μάθησης, ακολουθούμενη από την ερμηνεία των αποτελεσμάτων της. Στο τέλος παρουσιάζονται μερικά προβλήματα και προτάσεις που σχετίζονται με το μέλλον του χώρου των συστάσεων.A recommender system is a tool that filters information and suggests content to users which is relevant to their interests. Recommender systems have seen a rise in their use in the recent years as a result of the increasing internet use which provides researchers with huge amounts of user data. The purpose of this thesis is to study the various techniques that are applied to recommender systems as well as the deep learning models that are used to enhance those systems. Moreover, the evaluation methods of the recommender systems are described along with the challenges they face. Followingly, an implementa­ tion of a recommender system for video games which employs deep learning algorithms is provided followed by the interpretation of the results. At the end, some concerns and suggestions about the future in the field of recommendations are mentioned

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models

A novel hybrid recommendation system for library book selection

Abstract. Increasing number of books published in a year and decreasing budgets have made collection development increasingly difficult in libraries. Despite the data to help decision making being available in the library systems, the librarians have little means to utilize the data. In addition, modern key technologies, such as machine learning, that generate more value out data have not yet been utilized in the field of libraries to their full extent. This study was set to discover a way to build a recommendation system that could help librarians who are struggling with book selection process. This thesis proposed a novel hybrid recommendation system for library book selection. The data used to build the system consisted of book metadata and book circulation data of books located in Joensuu City Library’s adult fiction collection. The proposed system was based on both rule-based components and a machine learning model. The user interface for the system was build using web technologies so that the system could be used via using web browser. The proposed recommendation system was evaluated using two different methods: automated tests and focus group methodology. The system achieved an accuracy of 79.79% and F1 score of 0.86 in automated tests. Uncertainty rate of the system was 27.87%. With these results in automated tests, the proposed system outperformed baseline machine learning models. The main suggestions that were gathered from focus group evaluation were that while the proposed system was found interesting, librarians thought it would need more features and configurability in order to be usable in real world scenarios. Results indicate that making good quality recommendations using book metadata is challenging because the data is high dimensional categorical data by its nature. Main implications of the results are that recommendation systems in domain of library collection development should focus on data pre-processing and feature engineering. Further investigation is suggested to be carried out regarding knowledge representation