A Survey on Session-based Recommender Systems <s> Markov Chain-based Approaches <s> We define a new approach to the query recommendation problem. In particular, our main goal is to design a model enabling the generation of query suggestions also for rare and previously unseen queries. In other words we are targeting queries in the long tail. The model is based on a graph having two sets of nodes: Term nodes, and Query nodes. The graph induces a Markov chain on which a generic random walker starts from a subset of Term nodes, moves along Query nodes, and restarts (with a given probability) only from the same initial subset of Term nodes. Computing the stationary distribution of such a Markov chain is equivalent to extracting the so-called Center-piece Subgraph from the graph associated with the Markov chain itself. Given a query, we extract its terms and we set the restart subset to this term set. Therefore, we do not require a query to have been previously observed for the recommending model to be able to generate suggestions. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> Markov Chain-based Approaches <s> Typical recommender systems adopt a static view of the recommendation process and treat it as a prediction problem. We argue that it is more appropriate to view the problem of generating recommendations as a sequential optimization problem and, consequently, that Markov decision processes (MDPs) provide a more appropriate model for recommender systems. MDPs introduce two benefits: they take into account the long-term effects of each recommendation and the expected value of each recommendation. To succeed in practice, an MDP-based recommender system must employ a strong initial model, must be solvable quickly, and should not consume too much memory. In this paper, we describe our particular MDP model, its initialization using a predictive model, the solution and update algorithm, and its actual performance on a commercial site. We also describe the particular predictive model we used which outperforms previous models. Our system is one of a small number of commercially deployed recommender systems. As far as we know, it is the first to report experimental analysis conducted on a real commercial site. These results validate the commercial value of recommender systems, and in particular, of our MDP-based approach. <s> BIB002 </s> A Survey on Session-based Recommender Systems <s> Markov Chain-based Approaches <s> In this paper, we propose Personalized Markov Embedding (PME), a next-song recommendation strategy for online karaoke users. By modeling the sequential singing behavior, we first embed songs and users into a Euclidean space in which distances between songs and users reflect the strength of their relationships. Then, given each user's last song, we can generate personalized recommendations by ranking the candidate songs according to the embedding. Moreover, PME can be trained without any requirement of content information. Finally, we perform an experimental evaluation on a real world data set provided by ihou.com which is an online karaoke website launched by iFLYTEK, and the results clearly demonstrate the effectiveness of PME. <s> BIB003 </s> A Survey on Session-based Recommender Systems <s> Markov Chain-based Approaches <s> The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty in precisely learning users' sequential information and personalizing the recommendation model. To this end, we resort to the Metric Embedding method for the recommendation, which avoids drawbacks of the Matrix Factorization technique. We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. We further develop a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance. Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the state-of-the-art next POI recommendation methods. <s> BIB004
Markov chain-based RS adopt Markov chains to model the transitions over items within sessions to predict the probable next item given a sequence of prior items in a session. To decrease the model complexity, most RS are built on first-order Markov chain, like BIB001 BIB004 BIB002 BIB003 . Next, we give the formulation of a basic Markov chain-based RS, followed by some extensions.