A Survey on Session-based Recommender Systems <s> Sequential Pattern-based Approaches <s> Sequential access pattern mining discovers interesting and frequent user access patterns from web logs. Most of the previous studies have adopted Apriori-like sequential pattern mining techniques, which faced the problem on requiring expensive multiple scans of databases. More recent algorithms that are based on the Web Access Pattern tree (or WAP-tree) can achieve an order of magnitude faster than traditional Apriori-like sequential pattern mining techniques. However, the use of conditional search strategies in WAP-tree based mining algorithms requires re-construction of large numbers of intermediate conditional WAP-trees during mining process, which is also very costly. In this paper, we propose an efficient sequential access pattern mining algorithm, known as CSB-mine (Conditional Sequence Base mining algorithm). The proposed CSB-mine algorithm is based directly on the conditional sequence bases of each frequent event which eliminates the need for constructing WAP-trees. This can improve the efficiency of the mining process significantly compared with WAP-tree based mining algorithms, especially when the support threshold becomes smaller and the size of database gets larger. In this paper, the proposed CSB-mine algorithm and its performance will be discussed. In addition, we will also discuss a sequential access-based web recommender system that has incorporated the CSB-mine algorithm for web recommendations. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> Sequential Pattern-based Approaches <s> In this paper we present a rule-based personalization framework for encapsulating and combining personalization algorithms known from adaptive hypermedia and recommender systems. We show how this personalization framework can be integrated into existing systems by example of the educational online board Comtella-D, which exploits the framework for recommending relevant discussions to the users. In our evaluations we compare different recommender strategies, investigate usage behavior over time, and show that a small amount of user data is sufficient to generate precise recommendations. <s> BIB002 </s> A Survey on Session-based Recommender Systems <s> Sequential Pattern-based Approaches <s> This study proposes a sequential pattern based collaborative recommender system that predicts the customer's time-variant purchase behavior in an e-commerce environment where the customer's purchase patterns may change gradually. A new two-stage recommendation process is developed to predict customer purchase behavior for the product categories, as well as for product items. The time window weight is introduced to produce sequential patterns closer to the current time period that possess a larger impact on the prediction than patterns relatively far from the current time period. This study is the first to propose time-decaying sequential patterns within a collaborative recommender system. The experimental results show that the proposed system outperforms the traditional collaborative system using a public food mart dataset and a synthetic dataset. <s> BIB003 </s> A Survey on Session-based Recommender Systems <s> Sequential Pattern-based Approaches <s> Customers' purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers' purchase behavior may vary over time. In contrast, the sequential rule-based recommendation method analyzes customers' purchase behavior over time to extract sequential rules in the form: purchase behavior in previous [email protected]?purchase behavior in the current period. If a target customer's purchase behavior history is similar to the conditional part of the rule, then his/her purchase behavior in the current period is deemed to be the consequent part of the rule. Although the sequential rule method considers the sequence of customers' purchase behavior over time, it does not utilize the target customer's purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based KNN-CF method. The proposed method uses customers' RFM (Recency, Frequency, and Monetary) values to cluster customers into groups with similar RFM values. For each group of customers, sequential rules are extracted from the purchase sequences of that group to make recommendations. Meanwhile, the segmentation-based KNN-CF method provides recommendations based on the target customer's purchase data for the current period. Then, the results of the two methods are combined to make final recommendations. Experiment results show that the hybrid method outperforms traditional CF methods. <s> BIB004 </s> A Survey on Session-based Recommender Systems <s> Sequential Pattern-based Approaches <s> The proposed system is mainly based on mining closed sequential web access patterns. Initially, the PrefixSpan algorithm is employed on the preprocessed web server log data for mining sequential web access patterns. Subsequently, with the aid of post-pruning strategy, the closed sequential web access patterns are discovered from the complete set of sequential web access patterns. Then, a pattern tree, a compact representation of closed sequential patterns, is constructed from the discovered closed sequential web access patterns. The Patricia trie based data structure is used in the construction of the pattern tree. For a given user’s web access sequence, the proposed system provides recommendations on the basis of the constructed pattern tree. The experimentation of the proposed system is performed using synthetic dataset and the performance of the proposed recommendation system is evaluated with precision, applicability and hit ratio <s> BIB005 </s> A Survey on Session-based Recommender Systems <s> Sequential Pattern-based Approaches <s> Based on the intuition that frequent patterns can be used to predict the next few items that users would want to access, sequential pattern mining-based next-items recommendation algorithms have performed well in empirical studies including online product recommendation. However, most current methods do not perform personalized sequential pattern mining, and this seriously limits their capability to recommend the best next-items to each specific target user. In this paper, we introduce a personalized sequential pattern mining-based recommendation framework. Using a novel Competence Score measure, the proposed framework effectively learns user-specific sequence importance knowledge, and exploits this additional knowledge for accurate personalized recommendation. Experimental results on real-world datasets demonstrate that the proposed framework effectively improves the efficiency for mining sequential patterns, increases the user-relevance of the identified frequent patterns, and most importantly, generates significantly more accurate next-items recommendation for the target users. <s> BIB006 </s> A Survey on Session-based Recommender Systems <s> Sequential Pattern-based Approaches <s> Many online shopping malls in which explicit rating information is not available still have difficulty in providing recommendation services using collaborative filtering (CF) techniques for their users. Applying temporal purchase patterns derived from sequential pattern analysis (SPA) for recommendation services also often makes users unhappy with the inaccurate and biased results obtained by not considering individual preferences. The objective of this research is twofold. One is to derive implicit ratings so that CF can be applied to online transaction data even when no explicit rating information is available, and the other is to integrate CF and SPA for improving recommendation quality. Based on the results of several experiments that we conducted to compare the performance between ours and others, we contend that implicit rating can successfully replace explicit rating in CF and that the hybrid approach of CF and SPA is better than the individual ones. <s> BIB007 </s> A Survey on Session-based Recommender Systems <s> Sequential Pattern-based Approaches <s> Personalized recommendation is attracting more and more attentions nowadays. There are many kinds of algorithms for making predictions for the target users, and among them Collaborative Filtering CF is widely adopted. In some domains, a user's behavior sequences reflect his/her preferences over items so that users who have similar behavior sequences may indicate they have similar preference models. Based on this fact, we discuss how to improve the collaborative filtering algorithm by using user behavior sequence similarity. We proposed a new Behavior Sequence Similarity Measurement BSSM approach. Then, different ways to combine BSSM with CF algorithm are presented. Experiments on two real test data sets prove that more precise and stable recommendation performances can be achieved. <s> BIB008 </s> A Survey on Session-based Recommender Systems <s> Sequential Pattern-based Approaches <s> The sequential pattern mining-based recommendation has recently become a popular research topic in the field of recommender system. However, this kind of methods usually relies on frequency counts of sequences, which makes low-frequency sequences contribute little for the final recommend results. To solve this problem, in this paper, we propose a weighted sequence similarity-based method, called Personalized Recommendation based on Sequence Similarity (PRSS), for personalized recommendation. First, item-sequence weight model is introduced, which can reflect different importance of different items to different sequences. Then, target users’ sequence is compared with historical sequences using similarity function. Finally, the maximal common subsequence is proposed to rank candidate sequences and make recommendation. Experimental results show that PRSS generates more accurate recommendation for the target users. <s> BIB009
Although similar to pattern-based RS, sequential pattern-based RS is different from the former mainly from two aspects: BIB002 . It mainly captures the inter-session dependencies for cross session recommendations (e.g., next-basket recommendations), which is different from pattern-based RS capturing intra-session dependencies for inner session recommendations (e.g., next item recommendations); (2) It takes the order over sessions into account and thus it is more suitable for sequrntial data. Similar to pattern/rule-based RS, sequential pattern-based RS also contain three stages: sequential pattern mining, sequence matching, and recommendation generation. Next, we give a formulation of sequential pattern-based RS. Given a sequence set Q = {q 1 , q 2 , ..., q |Q | }, in which a sequence q = {s 1 , s 2 , ..., s |q | }(q ∈ Q) is a set of sessions order by their unique timestamps and a user u's past sequence q u , a sequential pattern-based RS generates recommendations by matching q u to those sequential patterns mined on Q BIB006 . Specifically, let q u = {s 1 , s 2 , ..., s h } be a sequence of u and SP = {p 1 , p 2 , ..., p |S P | } be the set of sequential patterns mined on Q. For any pattern p ∈ SP, if the last session of q u belongs to p, i.e., p = {s 1 , s 2 , ..., s h , s l ...}, then pattern p is a relevant pattern for this specific recommendation and the items after s h in p, like items in s l , are candidate items. For each candidate item i c , its support is the sum of the support of all relevant patterns, formally: Lastly, those candidate items with top support values are recommended to user u. Except for the basic framework described above, there are various extensions to make a more reliable sequential pattern-based RS. A typical example is to utilize the user-related weighted sequential pattern mining for personalized recommendations. Each sequence is assigned a weight based on its similarity to those past sequences of the target user to personalize recommendations for specific users BIB009 BIB006 BIB008 . Another extension is to build a hybrid RS by combining sequential pattern mining and collaborative filtering BIB007 BIB003 BIB004 BIB008 . Thanks to the combination, both the dynamic individual patterns captured by the sequential pattern mining and the general preference modeled by the collaborative filtering are considered. From the application perspective, item recommendations in transactional data and web page recommendations in web access log data are two typical application domains of sequential pattern-based RS BIB005 BIB001 .