Memory-based Collaborative Filtering Research Articles

Personal Value-Based User Modeling Without Attribute Evaluation and its Application to Collaborative Filtering

This paper proposes a personal values modeling method that does not require attribute ratings. The proposed method is applied to memory-based and model-based collaborative filtering (CF) to demonstrate its effectiveness. A recent trend in CF is to introduce additional factors than interaction history. A rate matching rate (RMRate) has been proposed for modeling user’s personal values, and it has been shown to be effective in increasing diversity and recommending niche (long-tail or unpopular) items. However, RMRate needs an attribute-level evaluations in addition to rating (total evaluation) to items, which limits its applicability. To obtain users’ personal values model only from a rating matrix, this paper defines users’ personal values as their tendency to select popular/unpopular items and reputable/unreputable items. Ten attributes are proposed to model user’s personal values, all of which can be calculated from a rating matrix without additional information. Experimental results on four datasets show that the proposed attributes have different characteristics from the RMRate, and can improve precision, recall, and normalized discounted cumulative gain of memory-based CF and factorization machines. It is also shown that the proposed modeling method is useful for mitigating a cold-start problem.

Analyzing the misleading information on Covid-19 using MBCFWS4

The COVID-19 pandemic had a major effect on almost every area of human lives in the majority of the world's countries. Misinformation spreads quickly in the initial phases of the COVID-19 pandemic in different forms, such as fear, distortion, contraindication assumption, and others. False and misleading advertisements harm millions of individuals. In recent research, there are more advanced techniques have been used to address the misinformation about the COVID-19 pandemic. But they are self-reported and probabilistic data during the lockdown period. So, it was difficult to find the respondents' perceptions based on sharing the COVID-19 misinformation. This proposed work analyzed and filtered some optimized factors to analyze the misinformation such as fake reports, fake remedies, conspiracy, susceptibility, vaccine rumors, social media, vitamin D prevents corona, political corona, socio-Economic, and more side effects after getting vaccinated. Collaborative filtering (CF) is the most efficient recommender system, and it is extensively utilized by a broad range of research institutions and enterprises, as well as being used in practice. It consists of two types of CF namely Memory-based CF and Model-based CF. In this work, Memory-based CF recommendation algorithm is combined with a similarity measure called MBCFWS4 to analyze the similarity measure between the factors to conclude the most impact factor. The Primary and secondary dataset helps to identify the respondents' perception based on the COVID-19 misinformation. The efficiency comparison of the proposed work is measured in terms of Precision, Recall & F-measure and found that this analysis using MBCFWS4 is outperforming well than the others as MBCFWS4 predicted accurately and revealed the conclusion based on the COVID-19 misinformation.

Memory-based Collaborative Filtering on Twitter Using Support Vector Machine Classification

Nowadays, watching films at home is one of people's entertainment. Netflix is a service provider for watching films and provides many types of film genres. However, of the many films available, it makes users confused to choose which film to watch first. The solution to the problem is a system that provides recommendations for the best films to watch based on user ratings. Twitter is still people's favorite social media to express their feelings, thoughts, and criticisms. In this system, tweets serve as input data that will be processed into data with rating values. This research implemented a recommendation system based on user ratings from tweets using collaborative filtering combined with Support Vector Machine (SVM) classification and implemented it on user-based and item-based. The test results in this study show that Collaborative Filtering gets the best RMSE value results on item-based 0.5911 and 0.8162 on user-based. The Support Vector Machine (SVM) classification algorithm using hyperparameter tuning produces item-based values with a precision of 85.03% and recall of 90.71%, while user-based values with a precision of 87.75% and recall of 88.95%.

Memory-based Collaborative Filtering on Twitter Using Support Vector Machine Classification

Nowadays, watching films at home is one of people's entertainment. Netflix is a service provider for watching films and provides many types of film genres. However, of the many films available, it makes users confused to choose which film to watch first. The solution to the problem is a system that provides recommendations for the best films to watch based on user ratings. Twitter is still people's favorite social media to express their feelings, thoughts, and criticisms. In this system, tweets serve as input data that will be processed into data with rating values. This research implemented a recommendation system based on user ratings from tweets using collaborative filtering combined with Support Vector Machine (SVM) classification and implemented it on user-based and item-based. The test results in this study show that Collaborative Filtering gets the best RMSE value results on item-based 0.5911 and 0.8162 on user-based. The Support Vector Machine (SVM) classification algorithm using hyperparameter tuning produces item-based values with a precision of 85.03% and recall of 90.71%, while user-based values with a precision of 87.75% and recall of 88.95%.

A New Item-Based Collaborative Filtering Algorithm to Improve the Accuracy of Prediction in Sparse Data

In memory-based collaborative filtering (CF) algorithms, the similarity and prediction method have a significant impact on the recommendation results. Most of the existing recommendation techniques have improved different similarity measures to alleviate inaccurate similarity results in sparse data, however, ignored the impact of sparse data on prediction results. To enhance the adaptability to sparse data, we propose a new item-based CF algorithm, which consists of the item similarity measure based vague sets and item-based prediction method with the new neighbor selection strategy. First, in the stage of similarity calculation, the Kullback–Leibler (KL) divergence based on vague sets is proposed from the perspective of user preference probability to measure item similarity. Following this, the impact of rating quantity is further considered to improve the accuracy of similarity results. Next, in the prediction stage, we relax the limit of depending on explicitly ratings and integrate more rating information to adjust prediction results. Experimental results on benchmark data sets show that, compared with other representative algorithms, our algorithm has better prediction and recommendation quality, and effectively alleviates the data sparseness problem.

Towards comprehensive approaches for the rating prediction phase in memory-based collaborative filtering recommender systems

Recommender systems play an indispensable role in today’s online businesses. In these systems, memory-based (neighborhood-based) collaborative filtering is an important strategy to predict items as expected by users. It consists of two phases: computing the preference similarity between each pair of users in the offline phase and predicting the rating of an active user for a target item in the online phase by aggregating ratings of his/her neighbors for the target item. Previous studies on memory-based collaborative filtering have heavily concentrated on proposing methods for the computation of user preference similarity. To further improve the performance of memory-based collaborative filtering, this paper is aimed at the rating prediction phase. By optimizing a proposed objective function, the method we used in the rating prediction phase helps more accurately estimate the weight between the active user and each of his/her neighbors. The experimental results show that the proposed method outperforms others, especially in the case of a small and medium number of selected neighbors.

Recommendation Algorithm Using Clustering-Based UPCSim (CB-UPCSim)

One of the well-known recommendation systems is memory-based collaborative filtering that utilizes similarity metrics. Recently, the similarity metrics have taken into account the user rating and user behavior scores. The user behavior score indicates the user preference in each product type (genre). The added user behavior score to the similarity metric results in more complex computation. To reduce the complex computation, we combined the clustering method and user behavior score-based similarity. The clustering method applies k-means clustering by determination of the number of clusters using the Silhouette Coefficient. Whereas the user behavior score-based similarity utilizes User Profile Correlation-based Similarity (UPCSim). The experimental results with the MovieLens 100k dataset showed a faster computation time of 4.16 s. In addition, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values decreased by 1.88% and 1.46% compared to the baseline algorithm.

EBCR: Empirical Bayes concordance ratio method to improve similarity measurement in memory-based collaborative filtering.

Recommender systems aim to provide users with a selection of items, based on predicting their preferences for items they have not yet rated, thus helping them filter out irrelevant ones from a large product catalogue. Collaborative filtering is a widely used mechanism to predict a particular user’s interest in a given item, based on feedback from neighbour users with similar tastes. The way the user’s neighbourhood is identified has a significant impact on prediction accuracy. Most methods estimate user proximity from ratings they assigned to co-rated items, regardless of their number. This paper introduces a similarity adjustment taking into account the number of co-ratings. The proposed method is based on a concordance ratio representing the probability that two users share the same taste for a new item. The probabilities are further adjusted by using the Empirical Bayes inference method before being used to weight similarities. The proposed approach improves existing similarity measures without increasing time complexity and the adjustment can be combined with all existing similarity measures. Experiments conducted on benchmark datasets confirmed that the proposed method systematically improved the recommender system’s prediction accuracy performance for all considered similarity measures.

Personalized recommendation: an enhanced hybrid collaborative filtering

Commonly used similarity-based algorithms in memory-based collaborative filtering may provide unreliable and misleading results. In a cold start situation, users may find the most similar neighbors by relying on an insufficient number of ratings, resulting in low-quality recommendations. Such poor recommendations can also result from similarity metrics as they are incapable of capturing similarities among uncommon items. For example, when identical items between two users are popular items, and both users rated them with high scores, their different preferences toward other items are hidden from similarity metrics. In this paper, we propose a method that estimates the final ratings based on a combination of multiple ratings supplied by various similarity measures. Our experiments show that this combination benefits from the diversity within similarities and offers high-quality personalized suggestions to the target user.

Improving the Prediction Quality in Memory-Based Collaborative Filtering Using Categorical Features

Despite years of evolution of recommender systems, improving prediction accuracy remains one of the core problems among researchers and industry. It is common to use side information to bolster the accuracy of recommender systems. In this work, we focus on using item categories, specifically movie genres, to improve the prediction accuracy as well as coverage, precision, and recall. We derive the user’s taste for an item using the ratings expressed. Similarly, using the collective ratings given to an item, we identify how much each item belongs to a certain genre. These two vectors are then combined to get a user-item-weight matrix. In contrast to the similarity-based weight matrix in memory-based collaborative filtering, we use user-item-weight to make predictions. The user-item-weights can be used to explain to users why certain items have been recommended. We evaluate our proposed method using three real-world datasets. The proposed model performs significantly better than the baseline methods. In addition, we use the user-item-weight matrix to alleviate the sparsity problem associated with correlation-based similarity. In addition to that, the proposed model has a better computational complexity for making predictions than the k-nearest neighbor (kNN) method.

Evaluation of Collaborative Filtering for Recommender Systems

Recently, due to the increasing amount of data on the Internet along with the increase in products’ purchasing via e-commerce websites, Recommender Systems (RS) play an important role in guiding customers to buy products they may prefer. Furthermore, these systems help the companies to advertise their products to the most potential customers, and therefore raise their revenues. Collaborative Filtering (CF) is the most popular RS approach. It is classified into memory-based and model-based filtering. Memory-based filtering is in turn classified into user-based and item-based. Several algorithms have been proposed for CF. In this paper, a comparison has been performed between different CF algorithms to assess their performance. Specifically, we evaluated K-Nearest Neighbor (KNN), Slope One, co-clustering and Non-negative Matrix Factorization (NMF) algorithms. KNN algorithm is representative of the memory-based CF approach (both user-based and item-based). The other three algorithms, on the other hand, are under the model-based CF approach. In our experiments, we used a popular MovieLens dataset based on six evaluation metrics. Our results reveal that the KNN algorithm for item-based CF outperformed all other algorithms examined in this paper.

Strategies of similarity propagation in web service recommender systems.

Recently, web service recommender systems have attracted much attention due to the popularity of Service-Oriented Computing and Cloud Computing. Memory-based collaborative filtering approaches which mainly rely on the similarity calculation are widely studied to realize the recommendation. In these research works, the similarity between two users is computed based on the QoS data of their commonly-invoked services and the similarity between two services is computed based on the common users who invoked them. However, most approaches ignore that the similarity calculation is not always accurate under a sparse data condition. To address this problem, we propose a similarity propagation method to accurately evaluate the similarities between users or services. Similarity propagation means that "if A and B are similar, and B and C are similar, then A and C will be similar to some extent". Firstly, the similarity graph of users or services is constructed according to the QoS data. Then, the similarity propagation paths between two nodes on the similarity graph are discovered. Finally, the similarity along each propagation path is measured and the indirect similarity between two users or services is evaluated by aggregating the similarities of different paths connecting them. Comprehensive experiments on real-world datasets demonstrate that our similarity propagation method can outstandingly improve the QoS prediction accuracy of memory-based collaborative filtering approaches.

Improving neighbor-based collaborative filtering by using a hybrid similarity measurement

Memory-based collaborative filtering is one of the recommendation system methods used to predict a user’s rating or preference by exploring historic ratings, but without incorporating any content information about users or items. It can be either item-based or user-based. Taking item-based Collaborative Filtering (CF) as an example, the way it makes predictions is accomplished in 2 steps: first, it selects based on pair-wise similarities a number of most similar items to the predicting item from those that the user has already rated on. Second, it aggregates the user’s opinions on those most similar items to predict a rating on the predicting item. Thus, similarity measurement determines which items are similar, and plays an important role on how accurate the predictions are. Many studies have been conducted on memory-based CFs to improve prediction accuracy, but none of them have achieved better prediction accuracy than state-of-the-art model-based CFs. In this paper, we proposed a new approach that combines both structural and rating-based similarity measurement. We found that memory-based CF using combined similarity measurement can achieve better prediction accuracy than model-based CFs in terms of lower MAE and reduce memory and time by using less neighbors than traditional memory-based CFs on MovieLens and Netflix datasets.

A Hybrid Model- and Memory-Based Collaborative Filtering Algorithm for Baseline Data Prediction of Friedreich's Ataxia Patients

Friedreich's ataxia (FRDA) is the most common inherited ataxia that causes progressive damage of nervous systems and performance deterioration of physical movements. FRDA baseline data analysis plays a crucial role in advancing the disease research, where the main obstacle comes from the baseline data collection primarily due to the degenerative symptoms of the FRDA patients. Inspired by the nowadays popular collaborative filtering (CF) method, a new FRDA baseline data collection algorithm is proposed in this article, with which the patients (or their families) are only required to provide certain reliable baseline data acquired from home and the uncertain/missing parts of the data can then be predicted with acceptable accuracy by utilizing existing patient information. The framework of the proposed algorithm is constructed based on a novel hybrid model combining the merits of model- and memory-based CF methods, thereby facilitating the baseline data collection with improved prediction accuracy. The proposed hybrid algorithm exhibits the following two main features: when a patient does not have neighbors sharing similar baseline data, the model-based CF component is activated to employ certain clustering method to find similar neighbors based on their attributes; and in the case that a patient does have neighbors, a novel similarity measure, which accounts for more statistical characteristics by integrating rating habits and degree of co-rated items, is developed in the memory-based component of the algorithm in order to adjust initial similarities between the patients. To evaluate the advantages of the proposed algorithm, the Scale for the Assessment and Rating of Ataxia is selected from the European FRDA Consortium for Translational Studies database. Experimental results demonstrate that our proposed hybrid CF approach is superior to other conventional approaches.

Applying landmarks to enhance memory-based collaborative filtering

Memory-based Collaborative Filtering (CF) has been a widely used approach for personalised recommendation with considerable success in many applications. An important issue regarding memory-based CF lies in similarity computation: the sparsity of the rating matrix leads to similarity computations based on few co-rated items between users, resulting in high sensitive predictions. Additionally, the ‘sparse’ similarity computation has high computational cost, due to the dimensionality of the item space. In this paper, we pursue both these issues. We propose a new model to compute similarity by representing users (or items) through their distances to preselected users, named landmarks. Such user modelling allows the introduction of more ratings into similarity computations through transitive relations created by the landmarks. Unlike conventional memory-based CF, the proposal builds a new user space defined by distances to landmarks, avoiding sensitivity in similarity computations. Findings from our experiments show that the proposed modelling achieves better accuracy than the ‘sparse’ similarity representation in all tested datasets, and has also yielded competitive accuracy results against the compared model-based CF algorithms. Furthermore, the proposed implementation has beaten all compared methods in terms of computational performance, becoming a promising alternative to memory-based CF algorithms for large datasets.

Sanitizing and measuring privacy of large sparse datasets for recommender systems

Big Data is characterized by large volumes of highly dynamical data and is used for discovering hidden trends and correlations. However, as more data is collected, previous pieces of information can be put together to facilitate linkage of private records. In this context, when protecting the privacy of data subjects, the same attributes that are to be protected may be used for further re-identification, that is, sensitive attributes may be used as quasi-identifiers. For example, in high-dimensional data such as recommendations, transaction records or geo-located data, previously published transactions and locations may be used to uncover further private transactions and locations. In this paper, we propose a k-anonymization algorithm and a metric for privacy in databases in which all the attributes are quasi-identifiers as well as sensitive attributes. We apply our algorithm on high dimensional datasets for model-based and memory-based collaborative filtering, and use the metric to perform privacy comparisons between different methods of protection such as k-anonymity and differential privacy. We show the applicability of our method by performing tests on the large and sparse dataset (MovieLens 20M) of 20 million ratings that 138,493 users gave to 27,278 movies.

An Efficient Similarity Measure for Collaborative Filtering

In the field of recommendation system, the memory-based Collaborative filtering has been proven to be useful in lots of practices. Similarity measures like Pearson correlation coefficient tend to only focus on improving as much as possible the accuracy. Handling datasets with different features, exiting measures cannot apply to different types of data simultaneously. In this paper, an improved similarity measure Common Pearson Correlation Coefficient (COPC) was proposed. Unlike existing measures, it strongly depends on chosen distance function, which adhere to the natural property of monotonicity and utilize consensus evaluation measure to capture an optimal value to improve PCC measure. To mitigate sparse problem, we also introduce the Hellinger Distance (Hg) as global similarity to lower the impact of lacking co-rated items. Experimental results on real-world datasets demonstrates that our measure outperformed the existing schemes of predicting ratings.

Intelligent Service Recommendation for Cold-Start Problems in Edge Computing

Memory-based collaborative filtering (i.e., MCF) is regarded as an effective technique to recommend appropriate services to target users. However, if recommendation data are very sparse in the edge environment, traditional MCF-based recommendation methods probably cannot output any recommended item (or service), i.e., a cold-start recommendation problem occurs. To cope with this cold-start problem, we propose an intelligent recommendation method named Inverse_CF_Rec. Concretely, for a target user, we first search for his/her opposite users (together referred to as “enemy” hereafter); afterward, we infer the possible friends of the target user indirectly according to Social Balance Theory; finally, optimal services are recommended to the target user based on the derived possible friends of the target user. The experiments are conducted on a real-world dataset WS-DREAM to validate the effectiveness and efficiency of our proposal. The experiment results show the advantages of our recommendation method in terms of recommendation accuracy and efficiency.

An improved memory-based collaborative filtering method based on the TOPSIS technique.

This paper describes an approach for improving the accuracy of memory-based collaborative filtering, based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. Recommender systems are used to filter the huge amount of data available online based on user-defined preferences. Collaborative filtering (CF) is a commonly used recommendation approach that generates recommendations based on correlations among user preferences. Although several enhancements have increased the accuracy of memory-based CF through the development of improved similarity measures for finding successful neighbors, there has been less investigation into prediction score methods, in which rating/preference scores are assigned to items that have not yet been selected by a user. A TOPSIS solution for evaluating multiple alternatives based on more than one criterion is proposed as an alternative to prediction score methods for evaluating and ranking items based on the results from similar users. The recommendation accuracy of the proposed TOPSIS technique is evaluated by applying it to various common CF baseline methods, which are then used to analyze the MovieLens 100K and 1M benchmark datasets. The results show that CF based on the TOPSIS method is more accurate than baseline CF methods across a number of common evaluation metrics.

Improving memory-based user collaborative filtering with evolutionary multi-objective optimization

The primary task of a memory-based collaborative filtering (CF) recommendation system is to select a group of nearest (similar) user neighbors for an active user. Traditional memory-based CF schemes tend to only focus on improving as much as possible the accuracy by recommending familiar items (i.e., popular items over the group). Yet, this may reduce the number of items that could be recommended and consequently weakens the chances of recommending novel items. To address this problem, it is desirable to consider recommendation coverage when selecting the appropriate group. This could help in simultaneously making both accurate and diverse recommendations. In this paper, we propose to focus mainly on the growing of the large search space of users’ profiles and to use an evolutionary multi-objective optimization-based recommendation system to pull up a group of profiles that maximizes both similarity with the active user and diversity between its members. In such manner, the recommendation system will provide high performances in terms of both accuracy and diversity. The experimental results on the Movielens benchmark and on a real-world insurance dataset show the efficiency of our approach in terms of accuracy and diversity compared to state-of-the-art competitors.