A multi-criteria trust-enhanced collaborative filtering algorithm for personalized tourism recommendations

A key challenge of the collaborative filtering (CF) information filtering is how to obtain the reliable and accurate results with the help of peers' recommendation. Since the similarities from small-degree users to large-degree users would be larger than the ones in opposite direction, the large-degree users' selections are recommended extensively by the traditional second-order CF algorithms. By considering the users' similarity direction and the second-order correlations to depress the influence of mainstream preferences, we present the directed second-order CF (HDCF) algorithm specifically to address the challenge of accuracy and diversity of the CF algorithm. The numerical results for two benchmark data sets, MovieLens and Netflix, show that the accuracy of the new algorithm outperforms the state-of-the-art CF algorithms. Comparing with the CF algorithm based on random walks proposed by Liu et al. (Int. J. Mod. Phys. C, 20 (2009) 285) the average ranking score could reach 0.0767 and 0.0402, which is enhanced by 27.3% and 19.1% for MovieLens and Netflix, respectively. In addition, the diversity, precision and recall are also enhanced greatly. Without relying on any context-specific information, tuning the similarity direction of CF algorithms could obtain accurate and diverse recommendations. This work suggests that the user similarity direction is an important factor to improve the personalized recommendation performance.

In view of the sparsity of user-resource interaction data, information asymmetry caused by cold start, and weak personalized resource allocation in the current integration of enterprises with vocational schools and universities, this article adopts the Collaborative Filtering (CF) algorithm to mine the user’s behavior patterns and preferences, establish a more intelligent resource recommendation model, and enhance the relevance and accuracy of resource recommendation. The intelligent resource recommendation system plays a key role in the construction of smart cities, fully optimizing the interaction between educational institutions and enterprises, and providing strong support for promoting the development of smart cities. First, an embedded feature expression for users and resources is established, and the deep embedding learning method is used to extract the association between high-dimensional and nonlinear features. Secondly, based on the traditional CF algorithm, the neural network architecture is combined to achieve accurate matching between users and resources. Finally, through experiments in user cold start and resource cold start scenarios, the performance improvement of the recommendation system is verified and compared with the widely used Matrix Factorization (MF) and Neural Collaborative Filtering (NCF) for analysis. According to the experimental results, the average precision of the proposed method is 0.804, which is 7.34% and 5.51% higher than MF and NCF respectively. The conclusion shows that the CF algorithm helps to enhance the resource recommendation effect and provides a new perspective for enhancing service personalization.

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.

In recent years, Recommender System (RS) research work has covered a wide variety of Artificial Intelligence techniques, ranging from traditional Matrix Factorization (MF) to complex Deep Neural Networks (DNN). Traditional Collaborative Filtering (CF) recommendation methods such as MF, have limited learning capabilities as it only considers the linear combination between user and item vectors. For learning non-linear relationships, methods like Neural Collaborative Filtering (NCF) incorporate DNN into CF methods. Though, CF methods still suffer from cold start and data sparsity. This paper proposes an improved hybrid-based RS, namely Neural Matrix Factorization++ (NeuMF++), for effectively learning user and item features to improve recommendation accuracy and alleviate cold start and data sparsity. NeuMF++ is proposed by incorporating effective latent representation into NeuMF via Stacked Denoising Autoencoders (SDAE). NeuMF++ can also be seen as the fusion of GMF++ and MLP++. NeuMF is an NCF framework which associates with GMF (Generalized Matrix Factorization) and MLP (Multilayer Perceptrons). NeuMF achieves state-of-the-art results due to the integration of GMF linearity and MLP non-linearity. Concurrently, incorporating latent representations has shown tremendous improvement in GMF and MLP, which result in GMF++ and MLP++. Latent representation obtained through the SDAEs' latent space allows NeuMF++ to effectively learn user and item features, significantly enhancing its learning capability. However, sharing feature extractions among GMF++ and MLP++ in NeuMF++ might hinder its performance. Hence, allowing GMF++ and MLP++ to learn separate features provides more flexibility and greatly improves its performance. Experiments performed on a real-world dataset have demonstrated that NeuMF++ achieves an outstanding result of a test root-mean-square error of 0.8681. In future work, we can extend NeuMF++ by introducing other auxiliary information like text or images. Different neural network building blocks can also be integrated into NeuMF++ to form a more robust recommendation model.

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.

Recommender systems play an important role in most modern e-commerce applications. They have allowed users to become aware of the myriad choices available to them. The ease of information and the abundance of options have helped users make educated decisions. A recommender system studies a user's preferences and continues learning the user's changing interests, so as to suggest items that incline with the user's interests. In cases where a user is new to the application, or the user prefers not to discourse preferences, the recommender system is unable to gather the user's preference on any item. This is called the cold start problem; wherein the system can make valid recommendations only once the user starts informing the system about his/her choices. In this paper, we discuss the challenges faced by the cold start problem and how this problem may be alleviated using social media. We suggest an approach where we collect public information from users' social media accounts and analyze this information to understand their preferences. In particular, we gather the new user's information using their Twitter profile; i.e., the user's interest and preferences are extracted from his/her Twitter profile by analyzing his/her tweets. These interests will help the system understand what kind of movies the user will be most interested in. We compare these preferences with the metadata about the individual items. Using this approach, we develop a movie recommendation system wherein we produce top-N movie recommendations for a user. We used the MovieTweetings dataset to model the application. Two sets of results have been produced. In the first, smaller set of 770 users, 72.67% of users have received 100% accurate movie recommendations while nearly 80% of users got more than 75% accuracy. For the second, larger set of more than 3,500 users, 53 % of users have received 100% accurate recommendations while 72% of users got more than 75% accuracy. These encouraging results have demonstrated that the approach is effectively in alleviating cold start problems in recommendation systems, and may be applicable to many other e-commerce applications.

An explicit trust and distrust clustering based collaborative filtering recommendation approach

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

Making recommendations from top-N user-item subgroups

CF (Collaborative filtering) algorithm has the widest and most successful applications in personalized recommendations. However, due to its over-reliance on the users' historical data, it is difficult to avoid data sparseness and cold start issues. The data sparseness and cold start may cause poor recommendation accuracy of the collaborative filtering algorithm. A hybrid optimal collaborative filtering algorithm based on users' characteristics and trust is proposed in this paper. In the process of users' similarity calculation, the age and gender of users' characteristics are introduced to make the determination of nearest neighbor more accurate. Besides, in order to improve the recommendation accuracy of the traditional CF recommendation algorithm, the trust relationship is introduced into the prediction score by measuring the users' trust, and this improvement will be used in the recommendation of new items in order to improve the recommendation accuracy of the traditional CF recommendation algorithm. The experimental results of Movie lens data set show that the improved recommendation accuracy of the recommendation system can be achieved by the proposed algorithm. Also, the problems of cold start and sparse data can be solved effectively.

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.

Collaborative filtering (CF), a technique used by recommendation systems, predicts and recommends items (information, products or services) that the user might like. Amazon.com's recommender system is one of the most famous examples of CF. Recommendation systems are popular in both commercial and research sectors, and they are applied in a variety of applications such as movies, music, books, social connections and venues. In particular, movie recommendation systems produce personal recommendations for movies. Existing CF algorithms employed in movie recommendation systems predict the unknown rating of a given user for a movie using only the ratings (i.e., preferences) of other like-minded users who have seen the movie. In such approaches, there exist certain limits in improving the accuracy of recommendation systems. This paper proposes an algorithm for movie recommendation that exploits the genre of the movie to enhance the accuracy of rating predictions. The proposed algorithm 1) numerically measures the correlation between movie genres using movie rating information; 2) classifies movies using the genre correlations and generates a list of recommended movies for the target user with the classified movies; and finally 3) predicts the ratings of the movies in the list using traditional CF algorithms. The experimental results show that the proposed algorithm yields higher accuracy in movie rating predictions than existing movie recommendation algorithms.

Nowadays, a recommendation system is an important technique in the development of electronic-commerce systems and the most popular approaches that use in many recommendation systems are a collaborative filtering algorithm. However, it still has problems such as scalability and sparse data. There are several previous methods used to deal with the weakness of collaborative filtering techniques such as a hybrid user model, but the results show their disadvantages in practical use. In this paper, we proposed a hybrid recommender system with review helpfulness features, which we have used to construct the hybrid model. In the experiment, the results of three recommendation techniques are compared: collaborative filtering based on hybrid user model, user-based collaborative filtering and our proposed method. The experimental results show that our proposed method is more efficient than other methods with the same dataset, in terms of accuracy. In addition, our proposed algorithm can decrease time consuming by constructing the hybrid model in the off-line phase and calculates the recommendation results in on-line phase.

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) Recommendation System is a prominent technology which is widely online. Large variety of available CF algorithms and the multitude of their possible parameters have a huge impact on quality of the outcome on ECommerce. Unfortunately, the literature on CF recommender system evaluation presents different evaluation metrics at different situation and could not provide any suggestion about the best. At the same time, it is a fact to accept that the accuracy measure should be different for each CF algorithm and depends upon the classification accuracy of that particular algorithm. As an initiative to address this problem in the present research paper, predictive accuracy metrics, classification accuracy metrics and rank accuracy metrics are considered as the classification accuracy metrics to know the overall competence for significant features of the chosen CF algorithms. At this juncture, normalization, a distinctive evaluation methodology, has been adopted to accomplish unique evaluation results of recommender systems. In this research paper, different accuracy metrics assessment would be brought into a common scale by taking into consideration of normalization process to evaluate metrics of the CF algorithms. A comprehensive comparative analysis is carried out and tabulated.