Item-based Collaborative Filtering Algorithm Research Articles

Application of SVM model based on collaborative filtering hybrid algorithm in e-commerce recommendation

ABSTRACT The current e-commerce suggestion suffers from information overload, which makes it harder for customers to choose products. Due to this, researchers created a hybrid collaborative filtering algorithm and support vector machine e-commerce recommendation model to improve user recommendations. The support vector machine for effective offline classification also explains how to pick the right label threshold, the sparsity of the dataset, and other challenges from both the user- and item-oriented perspectives. According to the experimental results, the support vector machine classification methods based on user perspective and items from the Movielens dataset had average accuracies of 0.91 and 0.88, respectively, while the corresponding average accuracies on the Jester-joke dataset were 0.74 and 0.75. Both the user- and item-based collaborative filtering algorithms on the popular goods dataset generated noticeably fewer mean absolute errors than the cold goods dataset. The average coverage and accuracy of recommendations made using an e-commerce recommendation model that incorporates support vector machines and collaborative filtering algorithms are 90.2%, 92.8%, 88.4%, 83.7%, and 80.2%, respectively. This showed that the research methodology produces positive results in e-commerce recommendation.

How Do Product Recommendations Help Consumers Search? Evidence from a Field Experiment

Product recommendations can benefit consumers’ online product search via multiple underlying mechanisms, such as showing products that offer them high value, facilitating navigation on the website, or exposing more product information. However, it is unclear ex ante which is the primary underlying mechanism that drives the benefits of product recommendations to consumers. We conducted a randomized field experiment to estimate the benefits of an item-based collaborative filtering (CF) recommendation system to consumers. We collect unique data on the affinity scores computed by an item-based CF algorithm to develop measures of a product’s net value and horizontal (taste) fit for consumers. Our results indicate that product recommendations help consumers search for higher-value products that are lower priced, fit their tastes better, or both. Besides that, we find that the ability to find higher-value products (rather than easy navigation or exposure to more product information) is the primary driver for consumers’ higher purchase probabilities under recommendations. We further find a higher benefit of recommendations in product categories with higher price dispersion and heterogeneity in consumers’ tastes, providing additional evidence for the lower price and better horizontal fit mechanisms. Finally, we find that when made available, consumers substitute their usage of other search tools on the website with product recommendations. Our findings have important implications for online retailers, policymakers, regulators, and item-based CF recommendation system design. This paper was accepted by D. J. Wu, information systems. Funding: This work was supported by the Public Utility Research Center of the University of Florida, the Hi! PARIS Fellowship, the HEC Foundation, and the Leavey School of Business at Santa Clara University [Grant 102720]. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2023.4951 .

RS on video games based on item-based collaborative filtering algorithm

RS on video games based on item-based collaborative filtering algorithm

Analysis of book circulation data and a book recommendation system in academic libraries using data mining techniques

The use of data mining modern technology in library management systems and information centers is of great importance. With the increasing availability of a large quantity of information, traditional tools and practices without wasting time and cost cannot respond to users accurately and quickly. The present study aims to analyze book circulation transactions and discover the user's book loan patterns to develop a recommender system. The data included 109,639 transactions and information from 8636 user records. Microsoft SQL Server and Matlab software were applied to analyze the data. Item-based collaborative filtering algorithms and decision tree methods were also applied. The results led to the extraction of rules for suggesting books to users. Analysis of the circulation data could be applied to address many issues like evaluation, collection acquisition policies, allocating funding for materials, and suggesting approaches to deselecting and allocating physical space for materials.

Analysis and research on the Algorithm of university book recommendation

This paper analyzes the performance of user-based collaborative filtering algorithm and item-based collaborative filtering algorithm in university library lending data and the applicable environment of different algorithms through accuracy and recall rate. The borrowing data of university library are processed by different methods and the processed data are run by different algorithms. The running results of different cutting ratios of training sets and test sets, different recommended quantities and different data processing are recorded. And then look for variables that are relevant. Finally, the best running environment of different recommendation algorithms is found by comparing the data.

Water Ecotourism Route Recommendation Model Based on an Improved Cockroach Optimization Algorithm

Aiming to address the problems of the current research on water ecotourism routes, a water ecotourism route recommendation model based on an improved cockroach optimization algorithm is proposed. The aim is to recommend the tour routes with the lowest exhaust emissions. Firstly, depending on tourists’ once-visited water scenic spots, a scenic spot recommendation model based on the improved item-based collaborative filtering algorithm is set up. Then, by combining the recommended scenic spots and integrating the random transportation modes selected by tourists, a tour route recommendation model based on an improved cockroach optimization algorithm is constructed, which can output the tour route that produces the lowest exhaust emissions. Finally, The sample experiment shows that, on the basis of combining with the multivariate random transportation modes, the proposed algorithm has greater advantages than the tour routes planned by the traditional electronic maps, as it can output the tour routes with the lowest exhaust emissions, reduce the damage exhaust emissions cause in the urban water environments and to water resources, and effectively protect the urban water ecological environments.

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.

Hybrid Recommendation Scheme Based on Deep Learning

Big data has been developed for nearly a decade, and the information data on the network is exploding. Facing the complex and massive data, it is difficult for people to get the demanded information quickly, and the recommendation algorithm with its characteristics becomes one of the important methods to solve the massive data overload problem at this stage. In particular, the rise of the e-commerce industry has promoted the development of recommendation algorithms. Traditional, single recommendation algorithms often have problems such as cold start, data sparsity, and long-tail items. The hybrid recommendation algorithms at this stage can effectively avoid some of the drawbacks caused by a single algorithm. To address the current problems, this paper makes up for the shortcomings of a single collaborative model by proposing a hybrid recommendation algorithm based on deep learning IA-CN. The algorithm first uses an integrated strategy to fuse user-based and item-based collaborative filtering algorithms to generalize and classify the output results. Then deeper and more abstract nonlinear interactions between users and items are captured by improved deep learning techniques. Finally, we designed experiments to validate the algorithm. The experiments are compared with the benchmark algorithm on (Amazon item rating dataset), and the results show that the IA-CN algorithm proposed in this paper has better performance in rating prediction on the test dataset.

Optimization of an Intelligent Music-Playing System Based on Network Communication

Streaming media server is the core system of audio and video application in the Internet; it has a wide range of applications in music recommendation. As song libraries and users of music websites and APPs continue to increase, user interaction data are generated at an increasingly fast rate, making the shortcomings of the original offline recommendation system and the advantages of the real-time streaming recommendation system more and more obvious. This paper describes in detail the working methods and contents of each stage of the real-time streaming music recommendation system, including requirement analysis, overall design, implementation of each module of the system, and system testing and analysis, from a practical scenario. Moreover, this paper analyzes the current research status and deficiencies in the field of music recommendation by analyzing the user interaction data of real music websites. From the actual requirements of the system, the functional and performance goals of the system are proposed to address these deficiencies, and then the functional structure, general architecture, and database model of the system are designed, and how to interact with the server side and the client side is investigated. For the implementation of data collection and statistics module, this paper adopts Flume and Kafka to collect user behavior data and uses Spark Streaming and Redis to count music popularity trends and support efficient query. The recommendation engine module in this paper is designed and optimized using Spark to implement incremental matrix decomposition on data streams, online collaborative topic model, and improved item-based collaborative filtering algorithm. In the system testing section, the functionality and performance of the system are tested, and the recommendation engine is tested with real datasets to show the discovered music themes and analyze the test results in detail.

An Improved Collaborative Filtering Recommendation Algorithm Based on Retroactive Inhibition Theory

Collaborative filtering (CF) is the most classical and widely used recommendation algorithm, which is mainly used to predict user preferences by mining the user’s historical data. CF algorithms can be divided into two main categories: user-based CF and item-based CF, which recommend items based on rating information from similar user profiles (user-based) or recommend items based on the similarity between items (item-based). However, since user’s preferences are not static, it is vital to take into account the changing preferences of users when making recommendations to achieve more accurate recommendations. In recent years, there have been studies using memory as a factor to measure changes in preference and exploring the retention of preference based on the relationship between the forgetting mechanism and time. Nevertheless, according to the theory of memory inhibition, the main factors that cause forgetting are retroactive inhibition and proactive inhibition, not mere evolutions over time. Therefore, our work proposed a method that combines the theory of retroactive inhibition and the traditional item-based CF algorithm (namely, RICF) to accurately explore the evolution of user preferences. Meanwhile, embedding training is introduced to represent the features better and alleviate the problem of data sparsity, and then the item embeddings are clustered to represent the preference points to measure the preference inhibition between different items. Moreover, we conducted experiments on real-world datasets to demonstrate the practicability of the proposed RICF. The experiments show that the RICF algorithm performs better and is more interpretable than the traditional item-based collaborative filtering algorithm, as well as the state-of-art sequential models such as LSTM and GRU.

A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation

With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user’s behaviour, which means a user’s interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn’t consider the time effect; next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method.

WooKong: A Ubiquitous Accelerator for Recommendation Algorithms with Custom Instruction Sets on FPGA

Recommendation algorithms, such as Neighborhood-based Collaborative- Filtering (CF), have been widely applied in various emerging machine learning applications. However, under the circumstance of the explosive big data, it poses significant challenges to CF recommendation algorithms as it is becoming quite time and energy-consuming. It has to be optimized and accelerated by powerful engines to process on large data scale. To solve these problems, in this article, we propose WooKong, a ubiquitous accelerator architecture for the collaborative-filtering recommendation on FPGA. It is able to accommodate three types of CF recommendation algorithms, including User-based CF, Item-based CF, and SlopeOne recommendations algorithms, with five different similarity analysis metrics including Jaccard, Cosine, CosineIR, euclidean, and Pearson. To maintain flexibility for these different CF algorithms and metrics, we adopt custom instruction sets to manipulate the learning and prediction accelerators. We implement a hardware prototype on a real Xilinx Zynq FPGA development board. Experimental results show that the proposed learning and prediction accelerators can achieve 8.0X speedup and 1.7X speedup compared with an Intel i7 processor respectively. The accelerator has the energy benefits of up to 137.4X compared with an NVIDIA Tesla K40C GPU, with the affordable hardware cost.

The recommendation service of the shareholding for fund companies based on improved collaborative filtering method

Through the effective processing of the financial dataset of listed companies and the improvement of the traditional collaborative filtering algorithm, this paper proposes a recommendation service which can predict the shareholding proportion of the listed companies more accurately in order to help fund companies to make reasonable decisions to invest listed companies. This service includes two important methods. To deal with the sparsity of data, one solution is to integrate similarity of user ratings and similarity of common-selected item to collaborative filtering algorithm. The relationship of common-selected item defined in this paper fully considers user’s common scored items. The greater the relationship value between users is, more similar two users are. The other solution is mainly applicable to new companies with very few indicators. The approach proposed in this paper is mainly based on the similarity between items, and generates different filling value for each unrated item based on re-filling with item-based collaborative filtering algorithm. From the perspective of the recommendation method itself, the method proposed in this paper is superior to the traditional standard collaborative filtering algorithm and the SVD algorithm.

Efficient collaborative filtering recommendations with multi-channel feature vectors

Feature vectors and similarity measures are the two key issues of most existing collaborative filtering (CF) algorithms. In item-based CF algorithms, the feature vector is often defined as the ratings of all users for a given item. For a recommender system with n users, m items, and c ratings, the length of the feature vector is n; hence, the time complexity of the similarity computation is O(n). Consequently, the overall time complexity is $$O(m^2n^2)$$ , which may be computationally prohibitive for recommender systems with millions of users. In this paper, we define the multi-channel feature vector (MCFV), which is a vector of channel length c, and calculate the similarity between items using the respective MCFVs. Each element of an MCFV corresponds to the number of users with respective ratings for the item. The time complexity for the similarity computation is O(c), and the overall time complexity is $$O(m^2nc)$$ when the k-nearest neighbors and weighted average algorithms are used. Experiments were conducted on four movie recommender systems, where n ranges from a few hundred to half a million, and c is five. Results show that the recommendation algorithms using our new similarity measure are significantly faster than their counterparts without sacrificing prediction accuracy in terms of mean absolute error and root mean square error.

A collaborative filtering recommendation algorithm based on the influence sets of e-learning group’s behavior

At present, due to use of nearest neighbor query algorithm based on memory, the traditional user-based collaborative filtering (CF) recommendation system has the shortages of poor expandability and lack of stability. On the aspect of expandability, the item-based CF algorithm was proposed but it still has not addressed the declined recommendation quality (poor stability) caused by sparse data. Being inspired by the concept of influence set, this paper proposes a new recommendation algorithm of CF-ISEGB (collaborative filtering based on the influence sets of e-learning group’s behavior). The influence sets of current e-learning group are used to improve the evaluation density of this resource, and the computation, prediction and rating methods are also defined for this new recommendation mechanism. The experimental result shows that compared to the traditional item-based CF algorithm which generates recommendation only based on the nearest neighbor, it can effectively alleviate the problem caused by sparse datasets and significantly improve the recommendation quality of recommendation system.

Recommending Locations Based on Users’ Periodic Behaviors

The research of location recommendation system is an important topic in the field of LBSN (Location-Based Social Network). Recently, more and more researchers began focusing on researching how to recommend locations based on user’s life behavior. In this paper, we proposed a new model recommending locations based on user’s periodic behaviors. In view of multiple periodic behaviors existing in time series, an algorithm which can mine all periods in time series is proposed in this paper. Based on the periodic behaviors, we recommend locations using item-based collaborative filtering algorithm. In this paper, we will also introduce our recommendation system which can collect users’ GPS trajectory, mine user’s multiple periods, and recommend locations based user’s periodic behavior.

Comparison of Collaborative Filtering Algorithms with Various Similarity Measures for Movie Recommendation

Collaborative Filtering is generally used as a recommender system. There is enormous growth in the amount of data in web. These recommender systems help users to select products on the web, which is the most suitable for them. Collaborative filtering-systems collect user’s previous information about an item such as movies, music, ideas, and so on. For recommending the best item, there are many algorithms, which are based on different approaches. The most known algorithms are User-based and Item-based algorithms. Experiments show that Item-based algorithms give better results than User-based algorithms. The aim of this paper isto compare User-based and Item-based Collaborative Filtering Algorithms with many different similarity indexes with their accuracy and performance. We provide an approach to determine the best algorithm, which give the most accurate recommendation by using statistical accuracy metrics. The results are compared the User-based and Item-based algorithms with movie recommendation data set.

Merging user and item based collaborative filtering to alleviate data sparsity

Memory based algorithms, generally referred as similarity based Collaborative Filtering (CF) algorithm, is one of the most widely accepted approaches to provide service recommendations. It provides personalized and automated suggestions to customers to select variety of products. Memory based algorithms mainly have two kinds of algorithms: User-based and Item-based algorithms. The User-based CF algorithm recommends items by finding similar users. Contrary to User-based CF, an Item-based CF algorithm recommends items by finding similar items. The core of memory based CF technologies is to calculate similarity among users or items. However, due to inherent sparsity, a large number of entries (ratings) in user-item rating matrix are missing. This results in only few available ratings to make prediction for the unknown ratings. This results in poor prediction quality of the CF algorithm. In this paper a hybrid approach is presented that combines user-based CF and item-based CF. It also leverage the biclustering technique to reduce the dimensionality. The biclustering helps to cluster all users/items into several groups. These clusters are then used to measure users/items similarities based on their respective parent groups. To obtain individual prediction, it adopts the user-based and item-based CF schemes based on the computed similarity respectively. Finally it combines the resultant predictions of each model to make final predictions. Interestingly, experiments demonstrated that the proposed approach outperforms the traditional user-based, item-based and some state of the art recommendation approaches in terms of accuracy of prediction and quality of recommendations.

Refine Item-Based Collaborative Filtering Algorithms with Skew Amplification

Case Amplification can improve the accuracy of a collaborative filtering (CF) algorithm with no extra space overhead by amplifying the effect of close candidates in the prediction. However, in a cold start scenario, the traditional Case Amplification on an item-based prediction can reduce accuracy. Given a small known set, Case Amplification can give a mediocre candidate an unsuitable amplification, by amplifying the numerator and the denominator in a predicting formula equally. We propose a skew amplification mechanism to address the problem: we amplify the numerator and the denominator differently. This reduces the effect of a mediocre but close item in the prediction. The balance between different amplifications is kept automatically by a controller, whose behavior depends on the size of the given set. Evaluation was carried out on four benchmarks, and results show that, in a cold-start scenario, skew amplification outperforms Case Amplification on boosting an item-based CF algorithm, especially when the given set becomes small.

Personalized Book Recommendation Based on Ontology and Collaborative Filtering Algorithm

Information recommendation service is one of important functions of digital library, aiming at the problem that book recommendation service exists the insufficient requirement mining of service object information in the current uni- versity library, personalized book recommendation method based on ontology information and collaborative filtering al- gorithm (abbreviated as OI-CFA algorithm) is proposed. Firstly, this paper discusses the necessity of collaborative rec- ommendation in digital library, introduces main methods and technology based on collaborative filtering recommendation system. However, there are several problems that are data sparse and new item forecast with collaborative filtering rec- ommendation method based on item. In order to solve these problems, this paper introduced an integrated similarity algo- rithms of structured semantic information based on OI-CFA. Extracting the semantic information of items including knowledge representation based on ontology, through ontology learning, the specified domain ontology is constructed. Compared with the traditional collaborative filtering algorithm and SVM, experimental results show that this method can not only solve the problems caused by the item-based collaborative filtering algorithm, but also improve the accuracy of recommendation.