A Survey on Session-based Recommender Systems <s> Session-based Recommendations With General User Preference <s> Modern recommender systems model people and items by discovering or `teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as product attributes, temporal information, or review text. However one important feature that is typically ignored by existing personalized recommendation and ranking methods is the visual appearance of the items being considered. In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets. We make use of visual features extracted from product images using (pre-trained) deep networks, on top of which we learn an additional layer that uncovers the visual dimensions that best explain the variation in people's feedback. This not only leads to significantly more accurate personalized ranking methods, but also helps to alleviate cold start issues, and qualitatively to analyze the visual dimensions that influence people's opinions. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations With General User Preference <s> This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our implemented, open-source (https://github.com/hexiangnan/sigir16-eals) eALS consistently outperforms state-of-the-art implicit MF methods. <s> BIB002 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations With General User Preference <s> Our work is generally focused on making recommendations for small or medium-sized e-commerce portals, where we are facing scarcity of explicit feedback, low user loyalty, short visit durations or a low number of visited objects. In this paper, we present a novel approach to use a specific user behavior pattern as implicit feedback, forming binary relations between objects. Our hypothesis is that if a user selects a specific object from the list of displayed objects, it is an expression of his/her binary preference between the selected object and others that are visible, but ignored. We expand this relation with content-based similarity of objects. We define implicit preference relation (IPR) a partial ordering of objects based on similarity expansion of ignored-selected preference relation. We propose a merging algorithm utilizing the synergic effect of two approaches this IPR partial ordering and a list of recommended objects based on any/another algorithm. We report on a series of offline experiments with various recommending algorithms on two real-world e-commerce datasets. The merging algorithm could improve the ranked list of most of the evaluated algorithms in terms of nDCG. Furthermore, we also provide access to the relevant datasets and source codes for further research. <s> BIB003 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations With General User Preference <s> Movie recommendation systems provide users with ranked lists of movies based on individual's preferences and constraints. Two types of models are commonly used to generate ranking results: long-term models and session-based models. While long-term models represent the interactions between users and movies that are supposed to change slowly across time, session-based models encode the information of users' interests and changing dynamics of movies' attributes in short terms. In this paper, we propose an LSIC model, leveraging Long and Short-term Information in Content-aware movie recommendation using adversarial training. In the adversarial process, we train a generator as an agent of reinforcement learning which recommends the next movie to a user sequentially. We also train a discriminator which attempts to distinguish the generated list of movies from the real records. The poster information of movies is integrated to further improve the performance of movie recommendation, which is specifically essential when few ratings are available. The experiments demonstrate that the proposed model has robust superiority over competitors and sets the state-of-the-art. We will release the source code of this work after publication. <s> BIB004 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations With General User Preference <s> Recommender systems try to predict which items a user will prefer. Traditional models for recommendation only take into account the user-item interaction, usually expressed by explicit ratings. However, in these days, web services continuously generate auxiliary data from users and items that can be incorporated into the recommendation model to improve recommendations. In this work, we propose an incremental Matrix Co-factorization model with implicit user feedback, considering a real-world data-stream scenario. This model can be seen as an extension of the conventional Matrix Factorization that includes additional dimensions to be decomposed in the common latent factor space. We test our proposal against a baseline algorithm that relies exclusively on interaction data, using prequential evaluation. Our experimental results show a significant improvement in the accuracy of recommendations, after incorporating an additional dimension in three music domain datasets. <s> BIB005 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations With General User Preference <s> Any learning algorithm for recommendation faces a fundamental trade-off between exploiting partial knowledge of a user»s interests to maximize satisfaction in the short term and discovering additional user interests to maximize satisfaction in the long term. To enable discovery, a machine learning algorithm typically elicits feedback on items it is uncertain about, which is termed algorithmic exploration in machine learning. This exploration comes with a cost to the user, since the items an algorithm chooses for exploration frequently turn out to not match the user»s interests. In this paper, we study how users tolerate such exploration and how presentation strategies can mitigate the exploration cost. To this end, we conduct a behavioral study with over 600 people, where we vary how algorithmic exploration is mixed into the set of recommendations. We find that users respond non-linearly to the amount of exploration, where some exploration mixed into the set of recommendations has little effect on short-term satisfaction and behavior. For long-term satisfaction, the overall goal is to learn via exploration about the items presented. We therefore also analyze the quantity and quality of implicit feedback signals such as clicks and hovers, and how they vary with different amounts of mix-in exploration. Our findings provide insights into how to design presentation strategies for algorithmic exploration in interactive recommender systems, mitigating the short-term costs of algorithmic exploration while aiming to elicit informative feedback data for learning. <s> BIB006
Siдni f icance. SBRS usually ignore the general user preference which can be well captured by other conventional RS like collaborative-filtering. This may lead to unreliable recommendations as users with different shopping preferences and consumption habits may chose different next item(s) even under the same session context. In this case, how to learn the general user preference from session data and then incorporate it into an SBRS model is critical yet challenging. Open issues. Here, we discuss two major issues w.r.t the general preference learning and its incorporation into SBRS and sketch the possibilities for future research directions. -How to incorporate a user's explicit preference into an SBRS? In this case, it is assumed that user preference on their purchased items is available in the form of a user-item preference matrix. An intuitive way is to first predict a user's preference on all candidate items by utilizing conventional approaches like matrix factorization (MF) and then use the predicted preference data as a constraint to tune the candidate ranking and selection in an SBRS. For instance, if two candidate items have similar possibilities to be chosen in the next choice according to the session context, the one with higher preference to a particular user can be put to the front of the others in the recommendation list. Another way is to combine the above two factors together in ranking the candidate items. In BIB004 , the authors proposed a generative adversarial network (GAN) framework to leverage the MF and RNN-based hybrid model for movie recommendations, which jointly learns long-term preference and short-term shopping patterns. -How to incorporate a user's experience into an SBRS without explicit preference data? In the real-word cases, the explicit preference data may not be always available as customers may not rate everything they bought. In this case, the shopping-basket-based transactional data is usually treated as a kind of implicit feedback BIB001 BIB002 to indicate user preference. Some existing work BIB005 BIB003 has explored how to learn a user's preference from such implicit feedback data in collaborative filtering. In practice, such data like clicks or views of items is often available in session-based recommdation scenarios BIB006 . Note that such implicit preference data is even the same as session data used in SBRS in some cases. Therefore, how to simultaneously learn the a user's implicit preference and the short-term patterns from an identical data source but avoid information duplication is a challenging issue which has not got effective solutions in SBRS.