A Survey on Session-based Recommender Systems <s> Model-Free 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> BIB001 </s> A Survey on Session-based Recommender Systems <s> Model-Free Approaches. <s> Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among customers' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are inefficient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few ruless this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm specifically designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be specified in advance. Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range. Rules are mined for a specific target user, reducing the time required for the mining process. We employ associations between users as well as associations between items in making recommendations. Experimental evaluation of a system based on our algorithm reveals performance that is significantly better than that of traditional correlation-based approaches. <s> BIB002 </s> A Survey on Session-based Recommender Systems <s> Model-Free 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> BIB003 </s> A Survey on Session-based Recommender Systems <s> Model-Free 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> BIB004 </s> A Survey on Session-based Recommender Systems <s> Model-Free 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> BIB005
Model-free approaches are mainly built on data mining techniques and they usually do not involve complex mathematical models. Two typical approaches in this branch are pattern/rule-based RS for unordered session data and sequential pattern-based RS for ordinal session data. (a) Pattern/Rule-based Approaches. Pattern/rule-based RS first mine frequent patterns or association rules and then use these patterns and rules to guide the subsequent recommendations. This is based on the assumption that most customers would follow the common shopping patterns BIB003 BIB002 BIB001 . For instance, customers usually bought milk and bread together when they go shopping, therefore {milk, bread} can be treated as a frequent pattern to recommend bread to those why have bought milk. It should be noted that pattern/rule-based RS are applied in unordered data. (b) Sequential Pattern-based Approaches. To handle those data having a strict order over items or involving time-factor based effect, sequential pattern-based RS are proposed. Similar to pattern-based RS, they first mine a collection of sequential patterns and then recommend the remaining items after the occurrence of the prior items  BIB004 BIB005 .