A Survey on Session-based Recommender Systems <s> Pattern/Rule-based Approaches <s> Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. In this study, we propose a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a highly condensed, much smaller data structure, which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent pattern mining methods. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> Pattern/Rule-based Approaches <s> To engage visitors to a Web site at a very early stage (i.e., before registration or authentication), personalization tools must rely primarily on clickstream data captured in Web server logs. The lack of explicit user ratings as well as the sparse nature and the large volume of data in such a setting poses serious challenges to standard collaborative filtering techniques in terms of scalability and performance. Web usage mining techniques such as clustering that rely on offline pattern discovery from user transactions can be used to improve the scalability of collaborative filtering, however, this is often at the cost of reduced recommendation accuracy. In this paper we propose effective and scalable techniques for Web personalization based on association rule discovery from usage data. Through detailed experimental evaluation on real usage data, we show that the proposed methodology can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy. <s> BIB002 </s> A Survey on Session-based Recommender Systems <s> Pattern/Rule-based Approaches <s> Abstract Web personalization has been providing electronic businesses with ways to keep existing customers and to obtain new ones. There are two approaches for providing personalized service: a content-based approach and a collaborative filtering approach. In the content-based approach, it is not easily applied to web objects (pages, images, sounds, etc) which are represented by multimedia data type information. Collaborative filtering approaches have cold-start problem. More serious weakness of collaborative filtering is that rating schemes can only be applied to homogenous domain information. In this paper, we present a framework of personalization expert by combining collaborative filtering method and association rule mining technique to overcome problems that traditional personalized systems have. Since multimedia data type web object cannot be easily analyzed, we adopted a collaborative filtering method that considers each object as an item, and attempts a personalized service. Similar users of each domain object are found as the result of the collaborative filtering method. These similar users’ web object access data is used by apriori algorithm to discover object association rules. <s> BIB003 </s> A Survey on Session-based Recommender Systems <s> Pattern/Rule-based Approaches <s> In many e-commerce applications, ranging from dynamic Web content presentation, to personalized ad targeting, to individual recommendations to the customers, it is important to build personalized profiles of individual users from their transactional histories. These profiles constitute models of individual user behavior and can be specified with sets of rules learned from user transactional histories using various data mining techniques. Since many discovered rules can be spurious, irrelevant, or trivial, one of the main problems is how to perform post-analysis of the discovered rules, i.e., how to validate user profiles by separating “good” rules from the “bad.” This validation process should be done with an explicit participation of the human expert. However, complications may arise because there can be very large numbers of rules discovered in the applications that deal with many users, and the expert cannot perform the validation on a rule-by-rule basis in a reasonable period of time. This paper presents a framework for building behavioral profiles of individual users. It also introduces a new approach to expert-driven validation of a very large number of rules pertaining to these users. In particular, it presents several types of validation operators, including rule grouping, filtering, browsing, and redundant rule elimination operators, that allow a human expert validate many individual rules at a time. By iteratively applying such operators, the human expert can validate a significant part of all the initially discovered rules in an acceptable time period. These validation operators were implemented as a part of a one-to-one profiling system. The paper also presents a case study of using this system for validating individual user rules discovered in a marketing application. <s> BIB004 </s> A Survey on Session-based Recommender Systems <s> Pattern/Rule-based Approaches <s> Recommender systems are being widely applied in many application settings to suggest products, services, and information items to potential consumers. Collaborative filtering, the most successful recommendation approach, makes recommendations based on past transactions and feedback from consumers sharing similar interests. A major problem limiting the usefulness of collaborative filtering is the sparsity problem, which refers to a situation in which transactional or feedback data is sparse and insufficient to identify similarities in consumer interests. In this article, we propose to deal with this sparsity problem by applying an associative retrieval framework and related spreading activation algorithms to explore transitive associations among consumers through their past transactions and feedback. Such transitive associations are a valuable source of information to help infer consumer interests and can be explored to deal with the sparsity problem. To evaluate the effectiveness of our approach, we have conducted an experimental study using a data set from an online bookstore. We experimented with three spreading activation algorithms including a constrained Leaky Capacitor algorithm, a branch-and-bound serial symbolic search algorithm, and a Hopfield net parallel relaxation search algorithm. These algorithms were compared with several collaborative filtering approaches that do not consider the transitive associations: a simple graph search approach, two variations of the user-based approach, and an item-based approach. Our experimental results indicate that spreading activation-based approaches significantly outperformed the other collaborative filtering methods as measured by recommendation precision, recall, the F-measure, and the rank score. We also observed the over-activation effect of the spreading activation approach, that is, incorporating transitive associations with past transactional data that is not sparse may "dilute" the data used to infer user preferences and lead to degradation in recommendation performance. <s> BIB005 </s> A Survey on Session-based Recommender Systems <s> Pattern/Rule-based Approaches <s> The numerous web sites existing nowadays make available more information than a user can manage. Thus, an essential requirement of current web applications is to provide users with instruments for personalized selective retrieval of web information. In this paper, a procedure for making personalized recommendations is proposed. The method is based on building a predictive model from an association model of Web data. It uses a set of association rules generated by a data mining algorithm that discovers knowledge in an incremental way. These rules provide models with relevant patterns that minimize the recommendation errors. <s> BIB006 </s> A Survey on Session-based Recommender Systems <s> Pattern/Rule-based Approaches <s> Web recommendation systems based on web usage mining try to mine users' behavior patterns from web access logs, and recommend pages to the online user by matching the user's browsing behavior with the mined historical behavior patterns. Recommendation approaches proposed in previous works, however, do not distinguish the importance of different pageviews, and all the visited pages are treated equally whatever their usefulness to the user. We propose to use pageview duration to judge its usefulness to a user, and try to give more consideration to more useful pageviews, in order to better capture the user's information need and recommend pages more useful to the user. In this paper we try to incorporate pageview weight into the Association Rule (AR) based model and develop a Weighted Association Rule (WAR) model. Comparative experiment of the two shows a significant improvement in the recommendation effectiveness with the proposed WAR model. <s> BIB007 </s> A Survey on Session-based Recommender Systems <s> Pattern/Rule-based Approaches <s> Due to the convenience of Internet, people can search for whatever information they need and buy whatever they want on the web. In the age of E-Commerce, it is difficult to provide support for customers to find the most valuable products that match their heterogeneous needs. Traditional approaches to this so-called personalization problem adopt predefined formats to describe the customer requirements. This always leads to distortion in eliciting requirement information and thus inaccurate recommendations. In this paper, we propose a personalized recommendation system using association rule mining and classification in e-commerce. Customer requirements are extracted from text documents and transformed into a set of significant phrases. Allowing the transformed transaction records, a set of association rule are mined from database using Apriori algorithm. CBA-CB algorithm is applied to produce the best rules out of the whole set of rules. The best classifiers are then generated after the test and validation of those rules, aimed to predict the item labels for new customer requirements and thus assigns the corresponding class labels to the customer. The system analysis and design of the proposed recommendation system as well as the implementation of prototype are also presented. <s> BIB008 </s> A Survey on Session-based Recommender Systems <s> Pattern/Rule-based Approaches <s> Standard memory-based collaborative filtering algorithms, such as k-nearest neighbor, are quite vulnerable to profile injection attacks. Previous work has shown that some model-based techniques are more robust than k-nn. Model abstraction can inhibit certain aspects of an attack, providing an algorithmic approach to minimizing attack effectiveness. In this paper, we examine the robustness of a recommendation algorithm based on the data mining technique of association rule mining. Our results show that the Apriori algorithm offers large improvement in stability and robustness compared to k-nearest neighbor and other model-based techniques we have studied. Furthermore, our results show that Apriori can achieve comparable recommendation accuracy to k-nn. <s> BIB009 </s> A Survey on Session-based Recommender Systems <s> Pattern/Rule-based Approaches <s> Web personalization is the process of customizing a web site to the needs of each specific user or set of users, taking advantage of the knowledge acquired through the analysis of the user’s navigational behavior. Personalized recommendation by predicting user-browsing behavior using association-mining technology has gained much attention in web personalization research area. However, the resulting association patterns did not perform well in prediction of future browsing patterns due to the low matching rate of the resulting rules and users’ browsing behavior. In this paper, we extend the traditional association rule problem by allowing a weight to be associated with each item in a transaction to reflect the interest/intensity of each item within the transaction. In turn, this provides us with an opportunity to associate a weight parameter with each item in a resulting association rule. We assign a significant weight to each page based on the time spent by user on each page and visiting frequency of each page, taking in to account the degree of interest instead of binary weighting. We present new personalized recommendation method base on the proposed weighted association-mining technique. We show, through experimentation on real data set that this approach results in more objective and representative predictions and shows a significant improvement in the recommendation effectiveness in comparison to the traditional association rule approaches. <s> BIB010 </s> A Survey on Session-based Recommender Systems <s> Pattern/Rule-based Approaches <s> Music recommendation is receiving increasing attention as the music industry develops venues to deliver music over the Internet. The goal of music recommendation is to present users lists of songs that they are likely to enjoy. Collaborative-filtering and content-based recommendations are two widely used approaches that have been proposed for music recommendation. However, both approaches have their own disadvantages: collaborative-filtering methods need a large collection of user history data and content-based methods lack the ability of understanding the interests and preferences of users. To overcome these limitations, this paper presents a novel dynamic music similarity measurement strategy that utilizes both content features and user access patterns. The seamless integration of them significantly improves the music similarity measurement accuracy and performance. Based on this strategy, recommended songs are obtained by a means of label propagation over a graph representing music similarity. Experimental results on a real data set collected from http://www.newwisdom.net demonstrate the effectiveness of the proposed approach. <s> BIB011
Pattern/rule-based RS mainly contain three stages: frequent pattern mining, session matching, and item recommendations. To be specific, given a item universal set I and the corresponding session set S over I as defined in Definition 2.2, a set of frequent patterns PT = {p 1 , p 2 , ..., p |PT | } are mined by using pattern mining algorithms like Apriori  or FP-Tree BIB001 . For a given active partial sessionŝ (e.g., a collection of chosen items in one transaction), if an itemî exists so thatŝ ∪î(î ∈ I \ŝ) is a frequent pattern, namely {ŝ ∪î} ∈ PT , then itemî is a candidate for recommendations. Further, if the conditional probability P(î |ŝ) is greater than a predifined confidence threshold β, thenî is added into the recommendation list BIB002 . Besides the aforementioned basic pattern/rule-based RS framework, there are many variants. Lin etc. [83] incorporated association rule mining into collaborative filtering. They replaced the minimum support constraint required in pattern mining with a constraint on the number of rules for each end user to improve the efficiency by avoiding mining too many rules irrelevant to a specific end user. To consider the different significance of different items and thus to recommend more useful ones, the methods in BIB007 and BIB010 utilized the page-view duration to weight the significance of each page and then incorporated such weight into association rule mining to build weighted association rule-based RS. By mining user behavior patterns, e.g., web navigation patterns, association rule mining is applied to capture the preference of a specific user or a group of users and thus help generate personalized recommendations BIB004 BIB010 BIB003 BIB008 . Some other works combined pattern mining into collaborative filtering to address some particular issues like sparsity, robustness and personalizition BIB005 BIB003 BIB009 . With regard to the application, except for the traditional shopping basket-based recommendations, pattern-based RS are commonly applied in web recommendations BIB006 , music recommendations BIB011 , and so on.