Recommender systems represent a critical field of AI technology applications. The core function of a recommender system is to recommend items of interest to users, but if it is only user history‐based (purchasing or browsing data), it can only recommend similar products to a user, which makes the user feel fatigued (creating so‐called “Information Cocoons”). Besides, transaction data (purchasing or browsing data) in various fields usually follow Pareto distributions. Accordingly, 20% of products are purchased or viewed a greater number of times (short‐head items), while the remaining 80% of products are purchased or viewed less frequently (long‐tail items). Using the traditional recommendation method, considering only the accuracy of recommendations, the coverage rate is relatively low, and most of the recommended items are short‐head items. The long‐tail item recommendation method not only considers the recommendation of short‐head items but also considers recommending more long‐tail items to users, thus improving the coverage and diversity of the recommendation results. Long‐tail item recommendation research has become a frontier issue in recommendation systems in recent years. While the current research paper is still scarce, there have been related research achievements in top‐level conferences in the field of computers, such as VLDB and IJCAI. Due to the fact that there is no review literature in this field, to allow readers to better understand the research status of the long‐tail item recommendation method, this paper summarizes the progress of the research on long‐tail item recommendation methods (from clustering‐based, which began in 2008, to deep learning‐based methods, which began in 2020) and the future directions associated with this research.

In recent years, the problem of $k$ -core has attracted wide-spread research attention due to the popularity of the graph-related applications, such as social network analysis, community detection, and collaboration networks. To efficiently support group-based activity planning, the organizers need to guarantee the size and closeness of the group. Current applications of the $k$ -core only support searching for the maximum $k$ -core group or adding the minimum number of edges to obtain the $k$ -core. However, no research has focused on the problem of enlarging the $k$ -core. In reality, when new tasks arrive, a work team needs to not only recruit new team members based on the requirements but also guarantee sufficient closeness among the members for good cooperation. In this paper, we first formalize a new query, a social group enlarging query with size constraints (SGEQ), which aims to find $n$ users to enlarge a subgraph from a $k$ -core of size $m$ to a ( $k+\Delta $ )-core of size ( $m+n$ ) by inserting the minimum number of edges. We prove that the SGEQ problem is NP-hard. To solve this problem, we first propose a novel algorithm, namely, the maximum connection edges algorithm (MCEA), which searches for the inserted vertex that has the most edges with an induced subgraph every time. Then, we develop an optimizing algorithm, namely, the maximum contribution degree algorithm (MCDA), which on average adds a number of edges in the expanded query less than that obtained by the MCEA. Finally, we conduct extensive experiments on two real-world datasets, and the results demonstrate the efficiency of the proposed algorithms.

The neighborhood-based recommendation method is a basic collaborative-filtering method, and one of the methods still used in the industry. The main basis for recommendation is the user's scoring data in the neighborhood-based recommendation method. However, due to the sparsity of user scoring data, the number of recommendations for long-tail items in the neighborhood-based method is relatively small. Although deep-learning technology has been widely applied to the neighborhood-based method, user scoring data are still needed in the methods, so the number of long-tail items in recommendation results is still very small. In order to increase the number of long-tail items recommended in the neighborhood-based method, this paper proposes a long-tail recommendation framework using frequent neighbors, and applies the framework to two real-world datasets. Experiments showed that the recommendation framework proposed in this paper effectively increases the number of long-tail items recommended in the neighborhood-based method and significantly improves the coverage metric.

Due to researchers'aim to study personalized recommendations for different business fields, the summary of recommendation methods in specific fields is of practical significance. News recommendation systems were the earliest research field regarding recommendation systems, and were also the earliest recommendation field to apply the collaborative filtering method. In addition, news is real-time and rich in content, which makes news recommendation methods more challenging than in other fields. Thus, this paper summarizes the research progress regarding news recommendation methods. From 2018 to 2020, developed news recommendation methods were mainly deep learning-based, attention-based, and knowledge graphs-based. As of 2020, there are many news recommendation methods that combine attention mechanisms and knowledge graphs. However, these methods were all developed based on basic methods (the collaborative filtering method, the content-based recommendation method, and a mixed recommendation method combining the two). In order to allow researchers to have a detailed understanding of the development process of news recommendation methods, the news recommendation methods surveyed in this paper, which cover nearly 10 years, are divided into three categories according to the abovementioned basic methods. Firstly, the paper introduces the basic ideas of each category of methods and then summarizes the recommendation methods that are combined with other methods based on each category of methods and according to the time sequence of research results. Finally, this paper also summarizes the challenges confronting news recommendation systems.