A Survey on Session-based Recommender Systems <s> Latent Markov <s> Digital storage of personal music collections and cloud-based music services (e.g. Pandora, Spotify) have fundamentally changed how music is consumed. In particular, automatically generated playlists have become an important mode of accessing large music collections. The key goal of automated playlist generation is to provide the user with a coherent listening experience. In this paper, we present Latent Markov Embedding (LME), a machine learning algorithm for generating such playlists. In analogy to matrix factorization methods for collaborative filtering, the algorithm does not require songs to be described by features a priori, but it learns a representation from example playlists. We formulate this problem as a regularized maximum-likelihood embedding of Markov chains in Euclidian space, and show how the resulting optimization problem can be solved efficiently. An empirical evaluation shows that the LME is substantially more accurate than adaptations of smoothed n-gram models commonly used in natural language processing. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> Latent Markov <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> BIB002 </s> A Survey on Session-based Recommender Systems <s> Latent Markov <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> BIB003
Embedding-based Approaches. Different from the basic Markov chain-based RS which calculate the transition probability based on the explicit observations directly, latent Markov Embedding (LME)-based RS first embed the Markov chains into a Euclidean space and then calculate the transition probabilities between items based on their Euclidean distance BIB001 . In this way, it can drive the unobserved transitions and thus solve the sparsity issue in limited observed data. Formally, each item i is represented as a vector v i in a d-dimensional Euclidean space, and the transition probability P(i (j−1) → i j ) is assumed to be negatively related to the Euclidean distance ||v i j − v i j−1 || 2 between items i j and i j−1 . Accordingly, the probability of an shopping path q ′ = {i 1 → i 2 →, ..., → i l } can be defined based on a Markov model: To generate personalized recommendations, Wu etc. BIB002 proposed a personalized Markov embedding (PME) model which maps both users and items into an Euclidean space where the user-item distance and item-item distance reflects the corresponding pairwise relationship respectively. Personalized ranking metric embedding is proposed in BIB003 to learn the embeddings by fitting the ranking of the POI transitions to make use of the unobserved data. Specifically, the observed next POI is assumed to be more related to the current one than those unobserved ones. For instance, if transition i j → i k is observed while i j → i t is not, i k should be ranked higher than i t in terms of i j , this is modeled as a ranking ">" over POIs. Therefore, the whole model is transferred into: