New collaborative recommendation approach based on concept lattice

[purposes] The recommendation effect of the user-based collaborative filtering algorithm is improved by extracting the effective dense sub-sequence between the user and the project and generating the data information of the project that the user participates in with high probability, The problem of data sparsity is alleviated to some extent, and the validity of binary data method in expressing user interest is verified. [methods]There are two steps to extract the effective dense subsequences from the user rate sequence, Firstly, the effective dense subsequence is extracted based on the time range; secondly, the subsequence is extracted twice by judging the items in the subsequence with high probability that do not conform to the user's interest. Generating data for projects with high probability of user participation requires the use of project attributes of user participation projects. The validity of the binary data method is mainly verified by the comparison experiment with the scoring data and the final result of the recommendation algorithm. [results] The experimental results show that RMSE (root mean square error) is reduced by more than 0.04 on the ml-latest-small data set compared with the classical traditional collaborative filtering algorithm. When F value (the harmonic mean of accuracy and recall rate) is taken as the evaluation standard, the algorithm proposed in this paper is more accurate than other classical algorithms, and the recommendation accuracy has been significantly improved. [conclusions] The method of extracting the effective dense subsequence between the user and the project is better than the traditional collaborative filtering method in terms of recommendation effect, and using binary data in the algorithm to replace the original rate data can bring better recommendation effect.

In order to tackle the problem of information overload and effective recommendation based on users' preference, need, and interest a number of research contributions has been made for the development of recommender systems. However, certain challenges, such as data sparsity, profiling attack, and black-box recommendation still exist and hamper their prediction accuracy. In this paper, we propose a hybrid approach to predict user ratings by incorporating both trust and context of the users in traditional recommender systems using collaborative filtering method. The similarity between two users is computed using both trust value and context-based similarity. The trust value is based on three trust statements -- rating deviation, emotions, and reviews helpfulness. On the other hand, context-based similarity is based on four contextual features -- companion, place, day, and priority. The performance of the proposed trust- and context-based hybrid approach is analyzed using mean absolute error and root mean square error on a real dataset generated from two movie data sources (IMDB and Rotten Tomatoes), and it performs significantly better in comparison to some of the standard baseline methods. The rating prediction using only trust statements gives better results in comparison to other collaborative filtering approaches, such as user-based and item-based filtering approaches. Similarly, context-based collaborative filtering approach also outperforms standard collaborative filtering approaches. In addition, rating prediction using both trust- and context-based features performs better in comparison to only trust-based or context-based approaches.

E-commerce Recommender Systems suggest useful and interesting products to customers in order to increase user satisfaction and online conversion rates. They typically exploit explicit or implicit user feedback such as ratings, buying records or clickstream data and apply statistical methods to derive recommendations. Collaborative filtering is one of the most widely used approaches in recommender systems. However, the traditional collaborative filtering methods compute users’ similarities in the dimension of products, and they do not take the influence of neighbor users into consideration when computing such similarities. This paper focuses on explicitly formulated user requirements as the sole type of user feedback. The new collaborative filtering method based on user interests’ transmission. This method computes customers’ similarities in the dimension of interests, and takes the interests transmission between different customers into reflection. This method not only can cope with cold start problem and data sparse problem, but also have prediction precision.

Collaborative filtering (CF) is one of the most successful approaches for making personalized recommendations. This paper aim sat the issue of tracking the drifting of the user's interests, and proposes a novel collaborative filtering recommendation method based on Ebbinghaus Forgetting Curve. The new method learns and tracks the user's interests by defining the user's interests as the short-term interest and the long-term interest, and by defining the weight function based on the time-window as well. In order to produce high quality recommendations, both the data weight based on the time-window and the data weight based on the item-similarity are used. Furthermore, the paper finds a special power function curve is much more fit to the forgetting curve through a mathematical analysis tool. Comparative experiments with the standard data show that the proposed method providing dramatically better quality recommendations.

The increasing abundance of content on the web has made information filtering even more important in helping users find information related to their interests. Personalization of web search is one such effort, that aims at improving the efficiency with which a user finds results relevant to his query. This is done by keeping track of a user's individual interests, and taking it into account while returning search results. We propose a robust user modeling technique that implicitly creates a Dynamic Category Interest Tree (DCIT), using a general ontology of the web and a set of web pages collected over time that give an insight into a user's interests. The DCIT is designed to use a fuzzy classification technique to keep track of what topics a user is interested in, his amount of interest in a topic, as well as reflect his changing interests overtime. The DCIT consists of a general ontology of the web, where each node represents a topic and consists of keywords that are usually used to describe that topic or category. Additional keywords that the user frequently associates with a topic, such as names of important people, organizations, or a specialized terminology, etc. Are also incorporated into the relevant topic. We use the Apriori Algorithm to extract these associated words from the user's web history in order to more accurately define the user's categories of interest. The DCIT is initially created by a content based approach using only the browsing history of the user, and is later further enhanced through collaborative filtering using the k-nearest neighbour-based algorithm. We propose a technique to re-rank the results from a search engine according to their relevance to a user, based on his implicitly learned DCIT. According to experimental results, our DCIT based ranking often outperforms search engines such as Google when it comes to retrieving web pages that are more relevant to a user's interest.

Nowadays, social network-based collaborative filtering (CF) methods are widely applied to recommend suitable products to consumers by combining trust relationships and similarities in the preference ratings among past users. However, these types of methods are rarely used for recommending manufacturing services. Hence, this study has developed a hybrid social network-based CF method for recommending personalized manufacturing services. The trustworthy enterprises and three types of similar enterprises with different features were considered as the four influential components for calculating predicted ratings of candidate services. The stochastic approach for link structure analysis (SALSA) was adopted to select top K trustworthy enterprises while also considering their reputation propagation on enterprise social network. The predicted ratings of candidate services were computed by using an extended user-based CF method where the particle swarm optimization (PSO) algorithm was leveraged to optimize the weights of the four components, thus making service recommendation more objective. Finally, an evaluation experiment illustrated that the proposed method is more accurate than the traditional user-based CF method.

In the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well‐known CF recommenders suffer from data sparsity, mainly in cold‐start scenarios, substantially reducing the quality of recommendations. In the vast literature about the aforementioned topic, there are numerous solutions, in which the state‐of‐the‐art contributions are, in some sense, conditioned or associated with traditional CF methods such as Matrix Factorization (MF), that is, they rely on linear optimization procedures to model users and items into low‐dimensional embeddings. To overcome the aforementioned challenges, there has been an increasing number of studies exploring deep learning techniques in the CF context for latent factor modelling. In this research, authors conduct a systematic review focusing on state‐of‐the‐art literature on deep learning techniques applied in collaborative filtering recommendation, and also featuring primary studies related to mitigating the cold start problem. Additionally, authors considered the diverse non‐linear modelling strategies to deal with rating data and side information, the combination of deep learning techniques with traditional CF‐based linear methods, and an overview of the most used public datasets and evaluation metrics concerning CF scenarios.

With the development of e-commerce, shopping on-line is becoming more and more popular. When we need to decide whether to purchase a product or not on line, the opinions of others become important. The convenience of new web technologies enables us to freely express our opinions and reviews for various products we have purchased which leads to a serious problem, information overloading. How to mine these review data to understand customers' preferences and make recommendations is crucial to merchants and researchers. Traditional collaborative filtering (CF) algorithm is one of the most successful recommendation system technologies. The core idea of CF algorithm is to recommend products based on other people who have similar tastes with target users. However, the ability of CF is limited by the sparsity problem, which is very common in reality. The reason derives from the fact that traditional CF method only takes users' ratings into account. In this paper, we propose a new personalized recommendation model, i.e. topic model based collaborative filtering (TMCF) utilizing users' reviews and ratings. We exploit extended LDA model to generate topic allocations for each review and then obtain each user's preference. Moreover, a new metric is designed to measure similarity between users alleviating the sparsity problem to a large extent. Finally, recommendations are made based on similar users' ratings. Experiments on seven data sets indicate better prediction accuracy than other traditional and state-of-the-art methods with substantial improvement in alleviating the sparsity problem.

In this paper, a new two levels propagated neighborhoods based collaborative filtering method (PNCF) is proposed for developing effective and efficient recommendation system. Traditional collaborative filtering (CF) algorithms focus on construct k-nearest neighborhood for each item/user from user-item purchase/rating matrix, such as item-based k-nearest-neighbor collaborative filtering method (itemKNN) and user-based k-nearest-neighbor collaborative filtering method (userKNN). However, the utilization of K-nearest neighborhood method for singe item/user always misses some nature neighbors due to inevitable data noise and data sparsity, resulting in poor prediction accuracy. A novel two levels propagated neighborhoods construction strategy is introduced in PNCF to complement traditional K-nearest neighborhood method, uncovering the underlying neighborhood relationship of each data sample. Furthermore, utilizing propagated neighborhoods improves the recommendation quality. Numerous experiments on MovieLens data set show the superiority of our approach over current state of the art recommendation methods.

Collaborative filtering (CF) approach is successfully applied in the rating prediction of personal recommendation. But individual information source is leveraged in many of them, i.e., the information derived from single perspective is used in the user-item matrix for recommendation, such as user-based CF method mainly utilizing the information of user view, item-based CF method mainly exploiting the information of item view. In this paper, in order to take full advantage of multiple information sources embedded in user-item rating matrix, we proposed a rating-based integrated recommendation framework of CF approaches to improve the rating prediction accuracy. Firstly, as for the sparsity of the conventional item-based CF method, we improved it by fusing the inner similarity and outer similarity based on the local sparsity factor. Meanwhile, we also proposed the improved user-based CF method in line with the user-item-interest model (UIIM) by preliminary rating. Second, we put forward a background method called user-item-based improved CF (UIBCF-I), which utilizes the information source of both similar items and similar users, to smooth itembased and user-based CF methods. Lastly, we leveraged the three information sources and fused their corresponding ratings into an Integrated CF model (INTE-CF). Experiments demonstrate that the proposed rating-based INTE-CF indeed improves the prediction accuracy and has strong robustness and low sensitivity to sparsity of dataset by comparisons to other mainstream CF approaches.

Recommendation systems have been widely used in many internet communities, as they could provide users with personalized items based on the big data analysis technologies. Collaborative Filtering (CF) recommendation algorithm is a well-known and widely used technique in the recommendation systems. However, traditional collaborative filtering methods usually make predictions only based on the rating-based features, which could not make a better understanding of user preferences. Moreover, with the growing disclosure of online personal information, privacy protection is another important issue to be considered in dynamic cyber networks. Thus, a security-aware prediction mechanism is necessary to deal with the data privacy in recommendation systems. In view of these challenges, this paper proposes a privacy-aware multi-preference-based collaborative filtering recommendation method with Locality-Sensitive Hashing (LSH). Firstly, two types of user representations are obtained based on both rating features and latent factors. To protect the privacy data of users, both the LSH technique and differential privacy data perturbation mechanism are integrated into our recommendation model. Besides, the employment of LSH in establishing users' nearest neighbor matrix also improve the similarity searching efficiency of the CF recommendation algorithm. Final recommendation is based on a SGD-based hybrid collaborative prediction model. Experimental results show that our proposed algorithm can improve the accuracy of the recommendation system and reduce the time cost on the basis of ensuring user privacy.

For personalized recommendations, collaborative filtering (CF) methods aim to recommend items to users based on data of historical user-item interactions. Deep learning has indicated success in improving performance of CF methods in recent works. However, to generate an item recommendation list for each user, a lot of deep learning based CF methods require every pair of users and items to be passed through multiple neural layers. This requires intensive computation and makes real-time end-to-end neural recommendations very costly. To address this issue, in this paper, we propose a new deep learning-based hierarchical decision network to filter out irrelevant items to save computation cost while maintaining good recommendation accuracy of deep CF methods. We also develop a distillation-based training algorithm, which uses a well-trained CF model as a teacher network to guide the training of the decision network. We conducted extensive experiments on real-world benchmark datasets to verify the effectiveness of efficiency of our decision network for making recommendations. The experimental results indicate that the proposed decision network is able to maintain or even improve the recommendation quality in terms of various metrics and meanwhile enjoy lower computational cost.

Collaborative filtering recommendation system is a widely used method of providing recommendations using explicit ratings on items from users, which provides personalized recommendations on products or services to customers. However, the current research on recommendation has paid little attention to the use of time-related data in the recommendation process and the study on collaborative filtering to reflect changes in user interest. This paper proposed a methodology for mining a userpsilas time interest change in order to improve the performance of collaborative filtering recommender algorithms. The methodology consists of four phases of calculating time weight for the ratings, improving Pearsonpsilas correlation, forming neighbors, and recommendations. Empirical results show our time-incorporated collaborative filtering recommender system is significantly more accurate than a pure collaborative filtering system.

SummaryWith the advent of Internet of Things (IoT) age, the variety and volume of web services have been increasing at a fast speed. This often leads to users' selections for web services more complicated. Under the circumstance, a variety of methods such as collaborative filtering are adopted to deal with this challenging situation. While traditional collaborative filtering method has some shortcomings, one of which is that only centralized user‐service data are considered while distributed quality data from multiple platform are ignored. Generally, service recommendation across different platforms often involves data communication among multiple platforms, during which user privacy may be disclosed and much computational time is required. Considering these challenges, a unique amplified locality‐sensitive hashing (LSH)‐based service recommendation method, that is, SRAmplified‐LSH, is proposed in the article. SRAmplified‐LSH can guarantee a good balance between accuracy and efficiency of recommendation and user privacy information. Finally, extensive experiments deployed on WS‐DREAM dataset validate the feasibility of our proposed method.

This paper presents the results obtained using a real e-learning recommender system where the collaborative filtering core has been adapted with the aim of weighting the importance of the recommendations in accordance with the users' knowledge. In this way, ratings from users with better knowledge of the given subject will have greater importance over ratings from users with less knowledge. In the same way, we validate the results obtained and we adjust, with just one parameter, the weight that should be awarded, in each specific e-learning recommender system, to the ratings of the users with the best reputation. The results obtained show a notable improvement regarding traditional collaborative filtering methods and suggest balanced weightings between the importance assigned to users with more or less knowledge.