(CF)2 architecture: contextual collaborative filtering

In the era of information abundance, the demand for personalized content recommendations has become paramount. Recommendation engines, particularly those employing collaborative filtering, play a pivotal role in delivering tailored suggestions based on user preferences. As technology evolves, the need to enhance the effectiveness of prediction algorithms within these engines becomes increasingly crucial. This research endeavors to contribute to this evolving landscape by delving into collaborative filtering methodologies, identifying challenges, and proposing novel strategies to elevate the accuracy and relevance of predictions in recommendation systems. Through this exploration, we aim to not only refine existing models but also pave the way for more sophisticated and reliable personalized content recommendations.
 This research aims to enhance prediction accuracy in recommendation engines utilizing collaborative filtering. Through an in-depth exploration of collaborative filtering techniques, we propose innovative approaches to improve the effectiveness of predictions. Our study addresses key challenges in collaborative filtering models, offering insights into refined algorithms and methodologies. By fine-tuning the collaborative filtering process, we anticipate a substantial boost in the overall performance of recommendation engines, ultimately advancing the field of personalized content suggestion. The simulation is performed using Java language and using two datasets Movie Lens 1M and Movie Lens 100K.The proposed model was evaluated using the Mean Absolute Error, Precision, and Recall.
 The proposed model achieved a mean absolute error value ranging between 0.78 and 0.84 using the Movie Lens 100K dataset, and a mean absolute error value ranging between 0.72 and 0.74 using the Movie Lens 1M dataset for different values of the number of user groups. As for precision and recall, the precision of the proposed model ranged between 0.97 and 0.985 using the Movie Lens 100K data set, and a precision value ranging between 0.944 and 0.954 using the Movie Lens 1M data set, also for different values of the number of user groups.
 As for the recall results, the proposed model achieved a recall value ranging between 0.755 and 0.85 using the Movie Lens 100K dataset, and a recall value ranging between 0.72 and 0.75 using the Movie Lens 100K dataset, also for different values of the number of user groups. These results were compared with the PMF, HPF, and NMF algorithms, where the proposed model proved its clear superiority over these algorithms. Using this analysis of the matrix allows us to obtain a good prediction accuracy of users' preferences and to find common groups of people with similar preferences.

Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based models are representative solutions in recommender systems; however, the content-based model has some shortcomings, such as single kind of recommendation results and lack of effective perception of user preferences, while for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this paper, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.

A decade has passed since the seminal Netflix Prize competition and Collaborative Filtering (CF) models are still at the forefront of Recommender System research. Significant progress has been achieved over this time, yet key aspects of the basic problem formulation have not been seriously challenged. Most state-of-the-art models still assume a supervised model in which the ultimate goal is to predict future user-item interactions based on the generalization of historical data.We wish to initiate a discussion on some key assumptions behind much of the mainstream research: What is the difference between predicting future user actions and optimizing Key Performance Indicators (KPIs)? Does a trade-off between accuracy and diversity really exist? Is supervised CF based on historical data still relevant in the age of modern reinforcement learning models such as contextual bandits? What evaluation metrics can be used prior to online experimentations and what are their limitations? Our main thesis is that at the core of all these issues lies a gap between most Collaborative Filtering models and the true objective of industry systems.

Existing methods of dynamic recommender systems usually build neural networks or collaborative filtering models according to the absolute timeline, which fails to consider the preferences of users or characteristics of products in their life cycles. With the rise of graph neural networks, researchers have recently proposed methods based on graph neural networks to solve the recommendation problem. However, such models do not consider evolving stages in their life cycles. In this paper, we study the dynamic recommendation problem and propose a novel model named Stage Evolving Graph Neural Network (SEGNN). SEGNN learns the life stage representation by the Gated Stage Attention Unit (GSAU) and then uses a fully connected layer as the rating score predictor. GSAU adopts two types of attention layers to learn the preferences of users and products within one coupled life stage and discover the preferences over life stages, respectively. GSAU also utilizes a memory mechanism to ensure that the evolutionary patterns are maintained in the final representations of users and products. Due to the lack of information caused by data sparsity, SEGNN designs a masked loss function to train the model. Extensive experiments are done on real-world datasets, and the experimental results show the effectiveness of our proposed model.

Long-term performance of collaborative filtering based recommenders in temporally evolving systems

Recommender systems are a class of personalized systems that aim at predicting a user’s interest in available services. Traditional collaborative filtering (CF) has proven to be one of the most successful techniques used in recommendation systems. However, the methods do not consider how the attribute features are related to user preferences, impacting the CF system’s prediction quality. To resolve the problem, this work proposes a novel collaborative filtering model derived from converting the user’s rating of an item to a distribution of attributes to the item. The proposed model is developed using the traditional similarity measure method. Finally, a series of experiments is performed on a typical data set, and the results indicate that the proposed model offers significant advantages in terms of improving the recommendation quality.

Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system

Traditional collaborative filtering (CF) based recommender systems tend to perform poorly when the user-item interactions/ratings are highly scarce. To address this, we propose a learning framework that improves collaborative filtering with a synthetic feedback loop (CF-SFL) to simulate the user feedback. The proposed framework consists of a recommender and a virtual user. The recommender is formulated as a CF model, recommending items according to observed user preference. The virtual user estimates rewards from the recommended items and generates a feedback in addition to the observed user preference. The recommender connected with the virtual user constructs a closed loop, that recommends users with items and imitates the unobserved feedback of the users to the recommended items. The synthetic feedback is used to augment the observed user preference and improve recommendation results. Theoretically, such model design can be interpreted as inverse reinforcement learning, which can be learned effectively via rollout (simulation). Experimental results show that the proposed framework is able to enrich the learning of user preference and boost the performance of existing collaborative filtering methods on multiple datasets.

As interest in eco-friendly transportation increases, the demand for electric vehicles (EVs) is also growing. With this trend, establishing an efficient charging infrastructure in urban environments has become essential. However, despite the presence of charging station infrastructure, its utilization remains low. In this study, we introduce a context-aware refining approach that incorporates location proximity and charging type preference into a Graph Neural Network (GNN)-based recommendation algorithm. For the EV charging station data, we collect and manage the large-scale real-time data. Additionally, to overcome the limitations posed by the absence or restricted access to driver profile data–an essential component for developing a recommendation system–we develop a simulator that analyzes and replicates real drivers’ charging patterns and behavioral characteristics. This enables us to generate realistic and reliable driver profile data. We employ the state-of-the-art GNN-based recommendation model for the base collaborative filtering. Then, we incorporate the user’s location proximity to the charging station and charging type preference (i.e., slow/fast) as context-aware refinement factors for the final recommendation results. To further enhance recommendation performance, we go beyond simple location proximity utilization by applying a clustering-based approach that reflects the actual spatial distribution, considering both charging stations and users. Through extensive experiments, we demonstrate that incorporating our contextual refinements consistently improves recommendation quality compared to the baseline GNN approach across different collaborative filtering models. In particular, leveraging charging type information and spatial clustering leads to substantial and stable performance gains, and their combined use yields the most robust results. These findings highlight the importance of jointly modeling functional preferences and geographical context for effective EV charging station recommendations.

Mobile context determination is an important step for many context-aware services such as location-based services, enterprise policy enforcement, building/room occupancy detection for power/HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the user is in &#x201C;Location X&#x201D;) are defined based on mobile context, it is paramount to verify the accuracy of the mobile context. Most of the existing solutions rely on context obtained directly from sensors which could be hacked, noisy or insufficient, which cannot be relied upon for security applications. In this article, we take a different approach by modeling mobile context based on past context data of related users and considering its unique challenges such as missing features. To this end, we propose three models for modeling mobile context based on symbolic time series data of feature-value pairs&#x2014;two stochastic models based on the theory of Hidden Markov Models&#x00A0;(HMMs) and one model based on deep learning&#x2014;<i>personalized model</i> (<i>HPContext</i>), <i>collaborative filtering model</i> (<i>(HCFContext)</i>), and <i>deep learning model</i> (<i>DeepContext</i>) to eventually replace <i>HPContext</i>. <i>HPContext</i> and <i>DeepContext</i> predict the current context using sequential history of the user&#x0027;s own past context observations; while <i>HCFContext</i> enhances the former with collaborative filtering features, which enables it to predict the current context of the primary user based on the context observations of users related to the primary user, e.g., same team colleagues in the company, gym friends, family members, etc. <i>DeepContext</i> models mobile context based on symbolic (i.e., categorical valued rather than continuous-valued) time series data using deep learning techniques. These models are then used to determine the context of the primary user at the current instant or some timesteps in to the future. Each of the proposed models can also be used to enhance or complement the context obtained from sensors. Finally, these models are thoroughly validated on a real-life dataset.

Purpose – The purpose of this paper is not only to design a more effective recommendation system for libraries, but also to eliminate many of the weaknesses found in the existing library recommender systems.Design/methodology/approach – A novel library recommender system was designed for English collections by integrating personal ontology model and collaborative filtering model with domain specification.Findings – The trend of the traditional library is evolving toward that of digital library. The personal ontology recommender (PORE) system offers a friendly user interface and provides several personalized services.Research limitations/implications – This system is only implemented and tested in the Library of National Chung Hsing University in Taiwan.Originality/value – The paper demonstrates a good methodology to offer an active, effective, and personalized recommendation system for library patrons.

The rapid growth of digital information across entertainment, e-commerce, and e-learning websites has created a pressing need for intelligent filtering tools that help users find relevant content.Recommendation systems address this challenge by predicting user preferences and suggesting items that align with individual interests.This research focuses on two significant paradigms of recommendation systems: Collaborative Filtering (CF) and Deep Learning (DL).Real datasets such as MovieLens and Amazon Reviews serve as the assessment foundation, while all experimental results are taken strictly from published peer-reviewed research.Classical CF models (User-based, Item-based, Matrix Factorization) are compared with state-of-the-art DL models, including AutoRec, Neural Collaborative Filtering (NCF), and Deep Matrix Factorization.Results from referenced studies demonstrate that deep learning models consistently outperform traditional CF approaches in accuracy, ranking metrics, and ability to capture non-linear user-item interactions.The report concludes that hybrid approaches combining CF and DL deliver the most robust performance for real-world systems.Detailed tables, graphs, diagrams, and real scientific data support the findings, ensuring the work remains authentic and academically grounded.

Because of its simplicity and effectiveness, collaborative filtering (CF) became one of the most successful recommendation algorithms. User-based CF is one classic method of CF algorithms. In order to solve the problem that common rating items are often too few to be used to effectively calculate the similarity of two users in user-based CF, we proposed an improved collaborative filtering model with item similarity called ISCF in this paper. In ISCF model, the similarity of items was considered in user-based collaborative filtering, which contributes to alleviate the problem of data sparsity and therefore calculate the similarity of user. Experimental results illustrate that our approach ISCF outperforms the average method and user-based CF. Compared with user-based CF, the average improvement in the percentage of ISCF at MAE and RMSE are 21.9% and 17.7%, respectively. In addition, our approach ISCF can predict more items than user-based CF, and the average improvement in the percentage of ISCF at prediction diversity is 33.86%.

Collaborative filtering predicts user preferences by modeling the user's historical behavior. Matrix factorization plays an important role in collaborative filtering. Matrix factorization use dot product to predicts rating. Nevertheless, the dot product does not satisfy the triangle inequality, which may limit their expressiveness and lead to sub-optimal solutions. This paper combines metric learning and distance factorization. Firstly, convert the scoring matrix to a distance matrix. Then the distance matrix is factorization by metric learning to obtain the position of users and items in low-dimensional Euclidean space. User preferences and distance are negatively correlated, the user is closer to the favorite item and farther away from the unloved item, that is, the user's preference is reflected by the distance. Experiments conducted on real-world data sets have shown that compared with classical algorithm, our method significantly improves the accuracy of recommendations.

Collaborative Filtering (CF), as one of the most popular approaches, is widely employed in recommender systems but suffers from the cold-start problem, where interactions are very limited for new users in the system. To deal with this issue, previous work has largely focused on utilizing various auxiliary information such as user profiles and social relationships to infer user preferences. However, the auxiliary information is not always available due to reasons such as user privacy concerns, making the CF approaches have to count on the limited interactions. Moreover, real-world situations require both accurate and quick recommendations for newly arrived users dynamically. Therefore, it is of critical importance to enable fast learning for new users during the training time of CF models. In this paper, we present a novel learning paradigm, named MetaCF, to learn an accurate CF model that makes fast adaptation on new users with limited interactions. Inspired by meta-learning, MetaCF treats the fast adaptation on a new user as a task and aims to learn a suitable model for initializing the adaption. To pursue a well-generalized model, MetaCF is equipped with a Dynamic Subgraph Sampling that accounts for the dynamic arrival of new users by dynamically generating representative adaptation tasks for existing users. Moreover, to stabilize the adaption procedure that faces the shortage of training samples, MetaCF further optimizes the learning rates for adaption in a fine-grained manner. MetaCF is applicable to any differentiable CF-based models where we demonstrate it on two representative ones, FISM [1] and NGCF [2]. Extensive experiments on three datasets validate the effectiveness of the proposed framework, which significantly outperforms state-of-the-art baselines by a large margin in the cold-start scenario where user-item interactions are limited.