top-N Recommendation Lists Research Articles

Point-of-Interest Recommendations Based on Immediate User Preferences and Contextual Influences

With the development of various location-based social networks (LSBNs), personalized point-of-interest (POI) recommendations have become a recent research hotspot. Current recommendation methods tend to mine user preferences from their historical check-in records but overlook interest deviations caused by real-time geographic environments and immediate interests present in the records, failing to meet users’ real-time and accurate needs. Therefore, this paper proposes a composite preference-based recommendation model (CPRM) for personalized POI recommendation. This method first extracts multi-factor contextual features, constructs a dual-layer attention network (DLAN) to capture long and short-term preferences, combines real-time geographic scenarios to uncover user immediate preferences, and then weights and fuses these three types of preferences to generate user composite preferences. Finally, a prediction function is employed to obtain the Top-N recommendation list. The experiments on two classic datasets, Foursquare and Gowalla, affirm the effectiveness of the model presented in this paper and offer a novel approach for providing personalized POI recommendations to users.

Product Personalized Recommendation Engine Based on Collaborative Filtering Algorithm

Nowadays, information plus Internet technology is continuously developing, and people have entered the era of information overload from the era of lack of information access. In this context, it is difficult to find one's own "tailor-made" information from the huge amount of information. In this context, the recommendation system came into being. This article uses the order data under an e-commerce platform, uses user-based and item-based collaborative filtering algorithms to personalize products, and builds a product recommendation system. Through the feasibility of actual application scenarios and the prediction score of the model, a series of evaluations were made on the algorithm, and reliable and effective prediction results were obtained from the three indicators of accuracy, recall and F1 score. The final recommendation effect of the TopN recommendation list obtained was significant, and the recommendation efficiency could reach 85%.Provide some ideas for the research and development direction of modern recommendation algorithms.

A Deep Autoencoder-Based Hybrid Recommender System

Recommender systems build their suggestions on rating data, given explicitly or implicitly by users on items. These ratings create a huge sparse user-item rating matrix which opens two challenges for researchers on the field. The first challenge is the sparsity of the rating matrix and the second one is the scalability of the data. This article proposes a hybrid recommender system based on deep autoencoder to learn the user interests and reconstruct the missing ratings. Then, SVD++ decomposition is employed, in parallel, to hold information of correlation between different features factors. Additionally, the authors gave a deep analysis of the top-N recommender list from the user's perspective to ensure that the proposed model can be used for practical application. Experiments showed that the proposed model performed better with high-dimensional datasets, and outperformed various hybrid algorithms in terms of RMSE, MAE, and in terms of the final top-N recommendation list.

Smart Campus Microservice Recommendation Algorithm Based on Neighborhood Clustering and Time Collaboration

The basic algorithm research of smart campus construction is the basic path for education to benefit from the development of science and technology. Based on the application environment of higher education teaching and management, according to the teaching periodicity and the closed-loop characteristics of the business system in universities, a neighborhood-based user attribute clustering algorithm is used in this paper to form user clusters, which are coupled micro-application clustering with periodic time. Finally, through normalized conversion and Pearson correlation coefficient analysis, the Top-N recommendation list is obtained, and a recommendation algorithm suitable for smart campus microservice system is constructed. In this paper, matrix sparsity and cold start are discussed, and data compensation is carried out by analyzing the general behavior patterns of teachers and students, which effectively solves the problem of insufficient recommendation list caused by matrix sparsity and cold start. Upon data analysis before and after the algorithm deployment, the algorithm effectively reduces the number of times that users use the search function.

Holistic Transfer to Rank for Top-N Recommendation

Recommender systems have been a valuable component in various online services such as e-commerce and entertainment. To provide an accurate top-N recommendation list of items for each target user, we have to answer a very basic question of how to model users’ feedback effectively. In this article, we focus on studying users’ explicit feedback, which is usually assumed to contain more preference information than the counterpart, i.e., implicit feedback. In particular, we follow two very recent transfer to rank algorithms by converting the original feedback to three different but related views of examinations, scores, and purchases, and then propose a novel solution called holistic transfer to rank (HoToR), which is able to address the uncertainty challenge and the inconvenience challenge in the existing works. More specifically, we take the rating scores as a weighting strategy to alleviate the uncertainty of the examinations, and we design a holistic one-stage solution to address the inconvenience of the two/three-stage training and prediction procedures in previous works. We then conduct extensive empirical studies in a direct comparison with the two closely related transfer learning algorithms and some very competitive factorization- and neighborhood-based methods on three public datasets and find that our HoToR performs significantly better than the other methods in terms of several ranking-oriented evaluation metrics.

Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings

Prior research has shown a robust effect of personalized product recommendations on user preference judgments for items. Specifically, the display of system-predicted preference ratings as item recommendations has been shown in multiple studies to bias users’ preference ratings after item consumption in the direction of the predicted rating. Top-N lists represent another common approach for presenting item recommendations in recommender systems. Through three controlled laboratory experiments, we show that top-N lists do not induce a discernible bias in user preference judgments. This result is robust, holding for both lists of personalized item recommendations and lists of items that are top-rated based on averages of aggregate user ratings. Adding numerical ratings to the list items does generate a bias, consistent with earlier studies. Thus, in contexts where preference biases are of concern to an online retailer or platform, top-N lists, without numerical predicted ratings, would be a promising format for displaying item recommendations.

Dynamic clustering collaborative filtering recommendation algorithm based on double-layer network

With the rapid development of internet economy, personal recommender system plays an increasingly important role in e-commerce. In order to improve the quality of recommendation, a variety of scholars and engineers devoted themselves in developing the recommendation algorithms. Traditional collaborative filtering algorithms are only dependent on rating information or attribute information. Most of them were considered in perspective of a single-layer network, which destroyed the original hierarchy of data and resulted in sparse matrix and poor timeliness. In order to address these problems and improve the accuracy of recommendation, dynamic clustering collaborative filtering recommendation algorithm based on double-layer network is put forward in this paper. Firstly, attribute information of users and items are respectively used to construct the user layer network and the item layer network. Secondly, new hierarchical clustering method is further presented, which separates users into different communities according to dynamic evolutionary clustering. Finally, score prediction and top-N recommendation lists are obtained by similarity between users in each community. Extensive experiments are conducted with three real datasets, and the effectiveness of our algorithm is verified by different metrics.

A novel Collaborative Filtering recommendation approach based on Soft Co-Clustering

Collaborative Filtering (CF) recommendation algorithm has been widely applied into recommender systems. Many CF algorithms associate a user/an item with one of subgroups by explicit or implicit features. However, considering that users may have multiple personalities and items may have diverse attributes, it is more reasonable to associate a user/an item with more than one group. In this paper, we propose the Soft K-indicators Alternative Projection (SKAP) algorithm, which can efficiently resolve soft clustering problem with high dimensions, to generate a sparse partition matrix and further a Top-N recommendation list is given. Unlike fuzzy C-means clustering, the SKAP algorithm is independent on the selection of initial values. In addition to that, we integrate the item type information into recommender systems to improve recommendation accuracy. Experimental results show that the proposed approach behaves superior performance in Top-N recommendation in terms of classical metrics and further show that multi-label classification framework is a better description than classical Co-Clustering framework.

Local ranking and global fusion for personalized recommendation

Most of top-n personalized recommendation algorithms perform a kind of global learning-to-rank based on the original user–item rating matrix, yet with an implicit presumption of a unified feature space for all users (all items). However, we argue that such a presumption is a limiting factor for better recommendation, because features learned in a global space could be too coarse-grained. As such, ranking a recommendation list against a unified space might be dominated by only a few significant aspects of users’ interests.In this paper, we propose a novel local ranking and global fusion (LRGF) framework for personalized recommendation, which allows using multiple fine-grained feature spaces for learning-to-rank. In LRGF, we first adopt two-stage random walk on the established user graph and item graph for selecting those mostly related users and items, so as to form multiple local user–item groups. Next, we propose a pairwise gap-aware Bayesian personalized ranking to obtain local ranking lists for each local user–item group. Finally, a global decision fusion is proposed to obtain the final top-n recommendation list for each user only from her local ranking lists. Experiments on the three real-world datasets show that the proposed LRGF outperforms the state-of-the-art schemes in terms of higher mean average precision and normalized discounted cumulative gain.

Improving Time-Aware Recommendations in Open Source Packages

Collaborative filtering techniques have been studied extensively during the last decade. Many open source packages (Apache Mahout, LensKit, MyMediaLite, rrecsys etc.) have implemented them, but typically the top-N recommendation lists are only based on a highest predicted ratings approach. However, exploiting frequencies in the user/item neighborhood for the formation of the top-N recommendation lists has been shown to provide superior accuracy results in offline simulations. In addition, most open source packages use a time-independent evaluation protocol to test the quality of recommendations, which may result to misleading conclusions since it cannot simulate well the real-life systems, which are strongly related to the time dimension. In this paper, we have therefore implemented the time-aware evaluation protocol to the open source recommendation package for the R language — denoted rrecsys — and compare its performance across open source packages for reasons of replicability. Our experimental results clearly demonstrate that using the most frequent items in neighborhood approach significantly outperforms the highest predicted rating approach on three public datasets. Moreover, the time-aware evaluation protocol has been shown to be more adequate for capturing the life-time effectiveness of recommender systems.

A framework for diversifying recommendation lists by user interest expansion

Recommender systems have been widely used to discover users’ preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N recommendation list involves not only relevance but also diversity. Most traditional recommendation algorithms are difficult to generate a diverse item list that can cover most of his/her interests for each user, since they mainly focus on predicting accurate items similar to the dominant interests of users. Additionally, they seldom exploit semantic information such as item tags and users’ interest labels to improve recommendation diversity. In this paper, we propose a novel recommendation framework which mainly adopts an expansion strategy of user interests based on social tagging information. The framework enhances the diversity of users’ preferences by expanding the sizes and categories of the original user-item interaction records, and then adopts traditional recommendation models to generate recommendation lists. Empirical evaluations on three real-world data sets show that our method can effectively improve the accuracy and diversity of item recommendation.

Enhancing Recommender Diversity Using Gaussian Cloud Transformation

The recommender systems community is paying great attention to diversity as key qualities beyond accuracy in real recommendation scenarios. Multifarious diversity-increasing approaches have been developed to enhance recommendation diversity in the related literature while making personalized recommendations to users. In this work, we present Gaussian Cloud Recommendation Algorithm (GCRA), a novel method designed to balance accuracy and diversity personalized top-N recommendation lists in order to capture the user's complete spectrum of tastes. Our proposed algorithm does not require semantic information. Meanwhile we propose a unified framework to extend the traditional CF algorithms via utilizing GCRA for improving the recommendation system performance. Our work builds upon prior research on recommender systems. Though being detrimental to average accuracy, we show that our method can capture the user's complete spectrum of interests. Systematic experiments on three real-world data sets have demonstrated the effectiveness of our proposed approach in learning both accuracy and diversity.