An improved collaborative filtering method based on similarity

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

Recommender systems can improve the quality of life in smart cities by presenting personalized services to the community. Such systems maintain a database of user profiles for producing recommendations for a specific user. The collaborative filtering (CF) approach used in these systems has become a benchmark approach for generating recommendations for interested users because it can provide “out of the box” solutions. These CF-based approaches first construct a user–item rating matrix and then exploit similarity methods. These approaches suffer from scalability, sparsity, and cold user conditions, which consequently result in the poor recommendation accuracy of these systems. To enhance the accuracy of recommender systems, social trust can play a vital role because people tend to interact with a system or respond positively to recommendations that originate from their social trustworthy friends. The proposed unified approach of this article uses explicit trust, implicit trust, and user preference similarity to create a unified rating profile for the target user to produce more powerful and accurate recommendations. The proposed unified approach also enhances the recommendation performance of CF-based recommender systems when only a limited set of ratings is available. Experiments are performed on three publicly available datasets which are FilmTrust, CiaoDVD, and Epinions. Comparison of obtained results is made with traditional similarity measures as well as up-to-date trust-based approaches. The results show that the proposed unified approach is superior to existing approaches in terms of both predictive and classification-based accuracy measures.

A new similarity measure for collaborative filtering based recommender systems

The rapid evolution of the Internet has resulted in the availability of huge volumes of online learning resources on the web. However, many learners encounter difficulties in retrieval of suitable online learning resources due to information overload. Besides, different learners have different learning needs arising from their differences in learner’s context and sequential access pattern behavior. Traditional recommender systems such as content based and collaborative filtering (CF) use content features and ratings, respectively, to generate recommendations for learners. However, for accurate and personalized recommendation of learning resources, learner’s context and sequential access patterns should be incorporated into the recommender system. Traditional recommendation techniques do not incorporate the learner’s context and sequential access patterns in computing learner similarities and providing recommendations; hence, they are likely to generate inaccurate recommendations. Furthermore, traditional recommender systems provide unreliable recommendations in cases of high rating sparsity. In this paper, we propose a hybrid recommendation approach combining context awareness, sequential pattern mining (SPM) and CF algorithms for recommending learning resources to the learners. In our recommendation approach, context awareness is used to incorporate contextual information about the learner such as knowledge level and learning goals; SPM algorithm is used to mine the web logs and discover the learner’s sequential access patterns; and CF computes predictions and generates recommendations for the target learner based on contextualized data and learner’s sequential access patterns. Evaluation of our proposed hybrid recommendation approach indicated that it can outperform other recommendation methods in terms of quality and accuracy of recommendations.

A recommender system uses specific algorithms and techniques in order to suggest specific services, goods or other type of recommendations that users could be interested in. User’s preferences or ratings are used as inputs and top-N recommendations are produced by the system. The evaluation of the recommendations is usually based on accuracy metrics such as the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE), while on the other hand Precision and Recall is used to measure the quality of the top-N recommendations. Recommender systems development has been mainly focused in the development of new recommendation algorithms. However, one of the major problems in modern offline recommendation system is the sparsity of the datasets and the selection of the suitable users Y that could produce the best recommendations for users X. In this paper, we propose an algorithm that uses Fuzzy sets and Fuzzy norms in order to evaluate the correlation between users in the data set so the system can select and use only the most relevant users. At the same time, we are extending our previous work about Reproduction of experiments in recommender systems by developing new explanations and variables for the proposed new algorithm. Our proposed approach has been experimentally evaluated using a real dataset and the results show that it is really efficient and it can increase both accuracy and quality of recommendations.

Majority of the e-commerce sites implement Recommender Systems (RS) to help users navigate through the large search space and assist their decision making process by suggesting products that the user may like. Collaborative Filtering (CF) is the most successful and widely used algorithm in the domain of RS. However, due to the exponential growth of the web in terms of both content and number of users, CF based RS face serious scalability issues. To alleviate this problem, we propose a clustering based CF approach using two hierarchical space partitioning data structures — K-d tree and Quadtree. We cluster or partition the users’ space of the system on the basis of user location and then use the resultant clusters for predicting ratings of a target user. Since the CF based recommendation algorithm is applied separately to the clusters and not on the entire rating data, it helps in bringing down the runtime of the algorithm substantially. We further measure spatial autocorrelation indices in the clusters to justify our clustering method. However, our objective is not only to reduce the runtime but also to maintain an acceptable recommendation quality. This requirement is rightly addressed by the proposed method which assures scalability, by processing very large datasets using the same computing resource. Moreover our proposed clustering scheme is oblivious of the underlying CF algorithm. Results from the extensive experiments conducted, show that our hierarchical clustering based recommendation approach reduces runtime of the standard CF algorithms by about 88%, 82%, 79% and 85% for MovieLens-100K, MovieLens-1M, Book-Crossing and TripAdvisor data respectively, while maintaining good recommendation quality.

A new item similarity based on [formula omitted]-divergence for collaborative filtering in sparse data

Collaborative filtering is the most widely used and successful technology for building recommender systems. However it faces challenges of scalability and recommendation accuracy. Collaborative filtering can be divided into memory based and model based. The former is more accurate while the latter performs better in scalability. This paper proposes a hybrid user model. The recommender system based on this model not only holds the advantage of recommendation accuracy in memory-based method, but also has the scalability as good as model-based method. The user model is constructed based on item combination feature and demographic information, and it focuses on searching for set of neighboring users shared with same interest, which helps to improve system scalability. To enhance recommendation accuracy, each feature in user model is given a different weight when computing the similarity between users. Genetic algorithm is adopted to learn the weight values of features. A comparison experiment was performed on MovieLens data set, and the result shows methodology proposed in this paper performs better than conventional collaborative filtering in recommendation accuracy and scalability.

Cooking your own food at home is a good step toward reducing fast food consumption. Fast food increases the risk of dangerous diseases. The diversity of recipe information available on the internet makes it difficult to choose recipes that match user preferences. Mobile technology can help with this by recommending recipes that better suit users' eating habits. This makes the transition to a healthier diet easier. Therefore, in this study, a recommendation system was developed that can recommend recipes based on the preferences of Android users. Two main recommendation methods are used in this study: content-based filtering and collaborative filtering. Using cosine similarity, a content-based recommendation system identifies the proximity between a recipe for food and its related context. The history of user comments on recipes serves as implicit feedback for the collaborative recommendation algorithm. This eliminates the need for explicit evaluations, such as ratings. This recommendation system generates recommendations in the form of the top ten food recipes with an evaluation matrix, referred to as NDCG@k and Hit-Ratio@k. The tests revealed that a content-based filtering technique may produce helpful recommendations, with the highest similarity score of 0.41 for the entry "chocolate cake that you can easily make at home." Meanwhile, in the collaborative filtering method using the Neural Collaborative Filtering (NCF) approach, the system shows consistent performance improvements, with the MAP@10 value increasing from 0.705 to 0.767 and the NDCG@10 from 0.78 to 0.83 after 10 training epochs. Keywords: Recommendation systems; content-based filtering; neural collaborative filtering; cosine similarity; implicit feedback

The object of research is the process of recommending items to the system user and improving the recommendation results. The principle of the recommendation system and the stages of creating recommendations are considered. The article describes various methods of filtering information to generate personalized recommendations. The advantages and disadvantages of each of the methods are revealed. Therefore, taking into account both theoretical and practical aspects, it is important to conduct research in order to correct the limitations characteristic of various filtering methods. A compact hybrid user model has been proposed for the collaborative filtering method of information. This model overcomes the limitations often encountered when using traditional approaches and allows for more efficient generation of personalized recommendations. For a visual example of the described approach, the recommender system of motion pictures was taken. This model combines user ratings with descriptions of the content of objects and uses the concept of genre interest rate, which was obtained by deriving formulas for calculating a hybrid feature, that is, an indicator of how much the user is interested in a particular genre. This facilitates the formation of a set of close associates for an active user. The proposed approach is aimed at ensuring high accuracy of recommendations in the system, reducing the requirements for computing resources and effectively using information about the content of objects. The hybrid model combines the benefits of memory-based collaborative filtering and model-based recommender systems, providing accuracy and scalability. Several examples with calculation results are given. A conclusion was drawn regarding the potential improvement of the quality of recommendations, taking into account the capabilities of the developed algorithm.

The influence of social relationships has received considerable attention in recommendation systems. In this paper, we propose a personalized advertisement recommendation system based on user preference and social network information. The proposed system uses collaborative filtering and frequent pattern network techniques using social network information to recommend personalized advertisements. Frequent pattern network is employed to alleviate cold-start and sparsity problems of collaborative filtering. For the social relationship modeling, direct and indirect relations are considered and relation weight between users is calculated by using six degrees of Kevin Bacon. Weight `1' is given to those who have connections directly, and weight `0' is given to those who are over six steps away and hove no relation to each other. According to a research of Kevin Bacon, everybody can know certain people through six depths of people. In order to improve prediction accuracy, we apply social relationship to user modeling. In our experiments, advertisement information is collected and item rating and user information including social relations are extracted from a social network service. The proposed system applies user modeling between collaborative filtering and frequent pattern network model to recommend advertisements according to user condition. User's types are composed with combinations of both techniques. We compare the performance of the proposed method with that of other methods. From the experimental results, a proposed system applying user modeling using social relationships can achieve better performance and recommendation quality than other recommendation systems.

The rise in the dimension and complexity of information generated in the clinical field has motivated research on the automation of tasks in personalized healthcare. Recommendation systems are a filtering method that utilizes patterns and data relationships to generate items of interest for a particular user. In healthcare, these systems can be used to potentiate physical therapy by providing the user with specific exercises for rehabilitation, albeit facing issues pertaining to low accuracy in earlier iterations (cold-start) and a lack of gradual optimization. In this study, we propose a physical activity recommendation system that utilizes a K-nearest neighbor (KNN) sampling strategy and feedback collection modules to improve the adequacy of recommendations at different stages of a rehabilitation period when compared to traditional collaborative filtering (CF) or human-constrained methods. The results from a trial show significant improvements in the quality of initial recommendations, achieving 81.2% accuracy before optimization. Moreover, the introduction of short-term adjustments based on frequent player feedback can be an efficient manner of improving recommendation accuracy over time, achieving overall better convergence periods than those of human-based systems, topping at a measured 98.1% accuracy at K = 7 cycles.

Now that we have so much information available, recommender systems are becoming more and more important in many different areas. Traditional methods are very popular because they work well, but they have problems with the cold-start problem and data sparsity. Large language models (LLMs) are a new breakthrough for recommender systems because they can understand and generate text in natural language. This study suggests a better recommendation method that uses both collaborative filtering and LLMs. This method combines the strengths of both methods, using collaborative filtering to model user preferences and LLMs to improve understanding of user and item information. This improves the accuracy and variety of recommendations. In this paper, we start by explaining the basic ideas behind collaborative filtering and LLMs. Then, we design a recommendation system that combines the two and test how well it works. The results show that the hybrid model based on collaborative filtering and LLM significantly improves accuracy, recall and user satisfaction, demonstrating its potential in complex recommendation scenarios.

Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities

Evaluation Of Recommendation System On A Movie Dataset