Imbibing Auxiliary Information Using Transfer Learning to Improve Collaborative Filtering-Based Recommender System

Recommender systems provide users with product information and suggestions, which has gradually become an important research tool in e-commerce IT technology, which has attracted a lot of attention of researchers. Collaborative filtering recommendation technology has been the most successful recommendation technology so far, but there are two major problems—recommendation quality and scalability. At present, research at home and abroad mainly focuses on recommendation quality, and there is less discussion on scalability. The scalability problem is that as the size of the system increases, the response time of the system increases to a point where users cannot afford it. Existing solutions often result in a significant drop in recommendation quality while reducing recommendation response time. In this paper, the clustering analysis subsystem based on the genetic algorithm is innovatively introduced into the traditional collaborative filtering recommendation system, and its design and implementation are given. In addition, when obtaining the nearest neighbors, only the clustered users of the target user are searched, making it a collaborative filtering recommender system based on genetic clustering. The experimental results show that the response time of the traditional collaborative filtering recommender system increases linearly with the increase in the number of users while the response time of the collaborative filtering recommender system based on genetic clustering remains unchanged with the increase in the number of users. On the other hand, the recommendation quality of the collaborative filtering recommender system based on genetic clustering is basically not degraded compared with that of the traditional collaborative filtering recommender system. Therefore, the collaborative filtering recommender system based on genetic clustering can effectively solve the scalability problem of the collaborative filtering recommender system.

Los sistemas de recomendación han tenido un rápido crecimiento tanto en investigación académica como en aplicaciones industriales en los últimos años. Las recomendaciones se nos proporcionan cuando elegimos elementos como música, películas, restaurantes, noticias, etc. Como parte de las técnicas de recomendación más prometedoras, los sistemas basados en recomendación de filtros colaborativos desempeñan un papel importante en las aplicaciones actuales. La aparición de nuevas fuentes de información requiere la combinación de sistemas de recomendación con técnicas avanzadas de otros campos, como el análisis de redes sociales, el procesamiento del lenguaje natural, el aprendizaje en profundidad, etc. Esta combinación mejora la calidad de las recomendaciones. Los sistemas de recomendación anteriores se centran principalmente en las mejoras de la precisión de la recomendación. Recientemente, surge una tendencia para la evaluación del rendimiento de los sistemas de recomendación que toma muchas propiedades nuevas en consideración. La fiabilidad es uno de los aspectos más notables y será profundamente estudiado en esta tesis. Se han propuesto algunas medidas para medir la fiabilidad de los sistemas de recomendación de filtrado colaborativo basados en la memoria. Sin embargo, observamos un área sin cubrir en el diseño de las medidas de fiabilidad adecuadas para los sistemas de recomendación de filtros colaborativos basados en modelos, especialmente aquellos basados en la factorización matricial. También encontramos que hay una falta de medidas de calidad de fiabilidad adecuadas. Esta tesis se concentra en proporcionar soluciones a ambas ausencias. Después de un estudio exhaustivo de trabajos de investigación del estado del arte, proponemos una nueva medida de fiabilidad que supera los desafíos de generalización y abstracción causados por los factores latentes de los métodos de factorización matricial. La medida propuesta se compara con con métodos y algoritmos representativos, usando bases de datos publicas, tanto académicas como comerciales. Los resultados experimentales muestran que nuestra nueva medida logra mejoras en la calidad de la recomendación. Además, la medida de fiabilidad propuesta también tiene un impacto muy positivo contra los "Shilling Attacks". Para obtener una mejora respecto a las medidas existentes de calidad de la fiabilidad, también proponemos, hasta donde sabemos, la primera medida de calidad de fiabilidad universal que es capaz de proporcionar valores numéricos para cualquier tipo de sistema de recomendación. Esta medida también se ha probado usando conjuntos de datos públicos reales y ha sido comparada con los métodos de calidad de la fiabilidad existentes. Basándonos en los resultados de los experimentos, concluimos que nuestra medida supera los últimos trabajos de investigación, proporcionando valores de calidad de fiabilidad más precisos e informativos. ----------ABSTRACT---------- Recommender systems have experienced a rapid growth in both academical research and industrial applications in latest years. Recommendations are provided to us when we choose items such as music, movies, restaurants, news, etc. As the most promising recommendation techniques, collaborative filtering recommender systems play an important role in current applications. The emergence of novel information sources requires to combine recommender systems with several advanced techniques coming from other fields like social network analysis, natural language processing, deep learning etc. This combination improves the quality of recommendations. Previous recommender systems research mainly focuses on the improvements of recommendation accuracy. Recently a new trend arises for the performance evaluation of recommender systems that takes many novel properties into consideration. Reliability is one of the most remarkable aspects, which will be deeply studied in this thesis. Some measures have been proposed for measuring reliability of memory based collaborative filtering recommender systems. However, we observe a gap in the design of proper reliability measures for model based collaborative filtering recommender systems, especially those based on matrix factorization. We also found that there is a lack in proper reliability quality measures. This thesis concentrates on providing solutions to both lacks. After a comprehensive study of state-of-the-art research works, we propose a novel reliability measure that overcomes the challenges of generality and abstraction caused by latent factors of matrix factorization methods. The proposed measure is tested against current baselines on public academic and commercial datasets; experiment results show that our novel measure achieves better improvements on recommendation quality. What’s more, the proposed reliability measure also has a very positive impact against shilling attacks. In order to fill in the blank of reliability quality measures, we also propose, as far as we know, the first universal reliability quality measure that is able to provide numerical values for any type of recommender systems. This measure has also been tested on public academic and commercial datasets using current methods. Based on the experiment results we conclude that our quality measure outperforms state-of-the-art research works by providing more accurate and informative reliability quality values.

Currently the use of web recommendation techniques is growing worldwide with aim of providing the custom-made required data to end users. Most of the web users complain about finding useful information on web sites. Web recommender systems predict the information needs of users and provide them with recommendations to facilitate their navigation. Recommender systems have been extensively explored in web mining. The web recommendation systems are split into two main categories such as collaborative recommendation system and content based recommendation system. However, the quality of recommendations and the user satisfaction with such systems are still not optimal. In adaptive association recommender systems based on web mining techniques have strengths and weaknesses. One of these weaknesses is lack of consideration of an appropriate measure to calculate the user’s interest degree of pages. Applying Fuzzy Association Rule Mining (FARM) algorithm to a recommender system leads to calculate user’s interest of pages more accurately. Consequently generated recommendations will be more desirable when compared to other association rule mining techniques.

Design of a dynamic and robust recommender system based on item context, trust, rating matrix and rating time using social networks analysis

Collaborative filtering (CF) technique in recommender systems (RS) is a well-known and popular technique that exploits relationships between users or items to make product recommendations to an active user. The effectiveness of existing memory based algorithms depend on the similarity measure that is used to identify nearest neighbours. However, similarity measures utilize only the ratings of co-rated items while computing the similarity between a pair of users or items. In most of the e-commerce applications, the rating matrix is too sparse since even active users of an online system tend to rate only a few items of the entire set of items. Therefore, co-rated items among users are even sparser. Moreover, the ratings a user gives an individual item tells us nothing about his comprehensive interest without which the generated recommendations may not be satisfactory to a user. In order to be able to address these issues, a comprehensive study is made of the various existing measures of similarity in a collaborative filtering recommender system (CFRS) and a hierarchical categorization of products has been proposed to understand the interest of a user in a wider scope so as to provide better recommendations as well as to alleviate data sparsity.

Collaborative filtering recommender systems primarily focus on short-term prediction accuracy but exhibit limitations concerning long-term user satisfaction and content diversity. In this paper, we reinterpret user-item interaction data as reinforcement learning with verifiable rewards and introduce the Group Relative Policy Optimization (GRPO) reinforcement learning algorithm, originally proposed in the large language model domain, to collaborative filtering model fine-tuning for the first time. GRPO directly updates policies without separate critic networks, balancing exploration and exploitation while optimizing long-term user engagement. In experiments conducted on Amazon review datasets covering baby products, video games, and industrial & scientific categories, the GRPO-optimized model achieved up to 15.16% improvement in Recall@10 compared to baseline models. Additionally, we revealed that user embeddings from graph-based collaborative filtering architectures positively contribute to GRPO algorithm optimization, whereas positional embeddings from sequential collaborative filtering architectures impede optimization performance. These findings empirically validate the effectiveness of the GRPO algorithm as a robust approach for recommender system model optimization.

With the rapid development of e-commerce, collaborative filtering recommendation system has been widely used in various network platforms. Using recommendation system to accurately predict customers’ preferences for goods can solve the problem of information overload faced by users and improve users’ dependence on the network platform. Because the recommendation system based on collaborative filtering technology has the ability to recommend more abstract or difficult to describe goods in words, the research related to collaborative filtering technology has attracted more and more attention. According to the past research, in collaborative filtering algorithm, if Pearson correlation coefficient is used, errors will occur under special circumstances. In this study, the normal recovery similarity measure is used to modify the similarity value to correct the error value of a collaborative filtering recommendation algorithm. Based on this, a big data analysis method based on a modified collaborative filtering recommendation algorithm is proposed. This research implemented it in the cloud Hadoop environment, and measure the execution time with 2, 5 and 8 nodes. Then the research compared it with the execution time of a single machine, and analyze its speedup ratio and efficiency. The experimental results show that the execution time increases with the number of neighbors. When the number of nodes is 5 and 8, the execution time is greatly improved, which improves the efficiency of collaborative filtering algorithm and can cope with massive data in the future.

Nowadays, collaborative filtering recommender systems have been widely deployed in many commercial companies to make profit. Neighborhood‐based collaborative filtering (CF) is common and effective. To date, despite its effectiveness, there has been little effort to explore their robustness and the impact of data poisoning attacks on their performance. Can the neighborhood‐based recommender systems be easily fooled? To this end, we shed light on the robustness of neighborhood‐based recommender systems and propose a novel data poisoning attack framework, encoding the purpose of attack and constraint against them. We first illustrate how to calculate the optimal data poisoning attack, namely, UNAttack. We inject a few well‐designed fake users into the recommender systems such that target items will be recommended to as many normal users as possible. Extensive experiments are conducted on three real‐world datasets to validate the effectiveness and the transferability of our proposed method. In addition, some interesting phenomena can be found. For example, (i) neighborhood‐based recommender systems with Euclidean distance‐based similarity have strong robustness and (ii) the fake users can be transferred to attack the state‐of‐the‐art CF recommender systems such as neural CF and Bayesian personalized ranking matrix factorization.

With the rapid development of artificial intelligence technology, recommendation systems have been widely applied in various fields. However, in the art field, art similarity search and recommendation systems face unique challenges, namely data privacy and copyright protection issues. To address these problems, this article proposes a cross-institutional artwork similarity search and recommendation system (AI-based Collaborative Recommendation System (AICRS) framework) that combines multimodal data fusion and federated learning. This system uses pre-trained convolutional neural networks (CNN) and Bidirectional Encoder Representation from Transformers (BERT) models to extract features from image and text data. It then uses a federated learning framework to train models locally at each participating institution and aggregate parameters to optimize the global model. Experimental results show that the AICRS framework achieves a final accuracy of 92.02% on the SemArt dataset, compared to 81.52% and 83.44% for traditional CNN and Long Short-Term Memory (LSTM) models, respectively. The final loss value of the AICRS framework is 0.1284, which is better than the 0.248 and 0.188 of CNN and LSTM models. The research results of this article not only provide an effective technical solution but also offer strong support for the recommendation and protection of artworks in practice.

Recommender Systems are used for finding the needle in the haystack. Recommender systems are of one the innovations in this revolution, which addresses the problem of filtering the data/information that is more likely to. Collaborative filtering is a technique which makes automatic predictions, by collecting the preferences for the users. But collecting the preferences from the user is no easy task. The Collaborative Filtering can also be a user based k-NN collaborative filtering algorithm or an item based k-NN collaborative filtering algorithm. Each type has its merits and demerits. Aiming at this concern, an ensemble based k-NN collaborative filtering is proposed. This work fuses the merits of the user based k-NN algorithm and item-based algorithm. The experiment analysis on the MovieLens dataset gives a consistent model that is accurate and results in better personalized movie recommendations than other conventional models. The experimental results show that ensemble based k-NN algorithm clearly improves enhances the recommender system's performance, and give better recommendation quality.

The selection of an interesting blog is becoming ever increasingly tough work for most users as the number of blogs is exponentially increasing every day. A Recommender System (RS) can help the user to find out the blog of his choice or based on parameters specified by him. The accuracy of RS to find out the right blogs for a particular user gets enhanced, if the text of blog is analyzed using semantic analysis. The RS uses the classification of blogs to find out the right blogs for a user. The information with respect to blogs like blog's title, tags given by blogger, blog image etc., provides good information to classify the blogs. A classification of text can be done using Part of Speech (POS) text analyzer. POS tagger of text provides information about the blogs that can be used to find the similarity between two blogs. The similar types of blogs can be kept together to provide better indexing in the set of large blogs. The paper finds the better accuracy of Collaborative Filtering Recommender System (CF RS) for bloggers when the blogs are processed with different text analysis using semantic information of blogs. The observation is addressed by computing various measures like Precision, Recall and F1.

“Shilling” attacks or “profile injection” attacks have always major challenges in collaborative filtering recommender systems (CFRSs). Many efforts have been devoted to improve collaborative filtering techniques which can eliminate the “shilling” attacks. However, most of them focused on detecting push attack or nuke attack which is rated with the highest score or lowest score on the target items. Few pay attention to grey attack when a target item is rated with a lower or higher scores than the average score, which shows a more hidden rating behavior than push or nuke attack. In this paper, we present a novel detection method to make recommender systems resistant to such attacks. To characterize grey ratings, we exploit rating deviation of item to discriminate between grey attack profiles and genuine profiles. In addition, we also employ novelty and popularity of item to construct rating series. Since it is difficult to discriminate between the rating series of attacker and genuine users, we incorporate into discrete wavelet transform (DWT) to amplify these differences based on the rating series of rating deviation, novelty and popularity, respectively. Finally, we respectively extract features from rating series of rating deviation-based, novelty-based and popularity-based by using amplitude domain analysis method and combine all clustered results as our detection results. We conduct a list of experiments on the Book-Crossing dataset in diverse attack models. Experimental results were included to validate the effectiveness of our approach in comparison with benchmarked methods.

Collaborative filtering recommender systems are essentially information systems which are capable of combining the judgment of a large group of people to make personalized recommendations and thereby alleviate the so-called information overload problem. However,collaborative filtering recommender systems are generally vulnerable to shilling attacks. Attackers can inject carefully chosen profiles into recommender systems in order to bias the recommendation results to their benefits. This may lead to a significant negative impact on the robustness of the systems. The main contribution of this paper is to build a probabilistic model for attack detection in the framework of probabilistic generative model. Experimental results show that this model can effectively detect shilling attacks of typical types.

Recent literature predicts that including context in a recommender system may improve its performance. The context-based recommendation approaches are classified as pre-filtering, post-filtering and contextual modeling. Little research has been done on studying whether including context in a recommender system improves the recommendation performance and no research has compared yet the different approaches to contextual RS. The research contribution of this work lies in studying the effect of the context on the recommendation performance and comparing a pre-filtering approach to a post-filtering using a collaborative filtering recommender system.

Recommender systems have become indispensable tools in various domains, such as e-commerce, entertainment, and social media, for delivering personalized user experiences. Collaborative Filtering (CF) is an essential technique in RS that leverages user similarity patterns to suggest items which align with individual preferences. This study presents an experimental comparative analysis of collaborative filtering-based recommender system methods including memory-based methods (KNN variants), model-based approaches (SVD, SVD++, co-clustering), and techniques based on neural networks (NCF, DeepFM, LightGCN). We conduct a thorough evaluation of these methods on the MovieLens benchmark datasets (100K, 1M, 25M) utilizing various metrics, such as RMSE, MAE, FCP, NDCG@10, Precision@10, Recall@10, and F1@10 Score, aiming to identify the most effective approaches and understand the advantages and disadvantages of each approach. Additionally, we provide detailed insights into the working mechanisms of each model. Our comprehensive analysis reveals the strengths and limitations of each method, offering critical insights for practitioners in selecting the most suitable recommender system technique based on specific requirements and constraints. The findings indicate that, on large datasets, neural and graph-based models achieve measurable improvements in both rating accuracy and top-k ranking tasks, with ranking gains observed upto 15%. Nonetheless, more straightforward approaches (KNN, SVD) continue to hold their ground in smaller datasets or low-resource environments because of their straightforward implementation and clarity in interpretation. The results highlight the importance of achieving a balance between computational expense, scalability, and model intricacy when choosing collaborative filtering techniques for practical implementations of recommender systems. We offer practical insights to assist professionals in selecting models that are suited to particular application needs and data attributes. This research enhances the understanding of collaborative filtering techniques and offers valuable insights for improving the performance of RS across diverse domains.