1. SIGIR Accepted Papers: http://sigir.org/sigir2018/accepted-papers/，今年有很多的推荐系统方面的论文。
2. Mathematical Notation for Recommender Systems by Michael Ekstrand，这些也是我经常使用的数学符号。
1. Explainable Recommendation: A Survey and New Perspectives
Yongfeng Zhang, Xu Chen
In recent years, a large number of explainable recommendation approaches -- especially model-based explainable recommendation algorithms -- have been proposed and adopted in real-world systems. 3) We summarize the application of explainable recommendation in different recommendation tasks, including product recommendation, social recommendation, POI recommendation, etc. We end the survey by discussing potential future research directions to promote the explainable recommendation research area.
2. A Missing Information Loss function for implicit feedback datasets
Juan Arévalo, Juan Ramón Duque, Marco Creatura
Missing information is often used as negative feedback. This is frequently done either through negative sampling (point-wise loss) or with a ranking loss function (pair- or list-wise estimation). In this paper we propose a novel objective function, the Missing Information Loss (MIL) function, that explicitly forbids treating unobserved user-item interactions as positive or negative feedback.
3. TR-SVD: Fast and Memory Efficient Method for Time Ranged Singular Value Decomposition
Jun-Gi Jang, Dongjin Choi, Jinhong Jung, U Kang
Singular value decomposition (SVD) is a crucial tool to discover hidden factors in multiple time series data, and has been used in many data mining applications including dimensionality reduction, principal component analysis, recommender systems, etc. In this paper, we propose TR-SVD (Time Ranged Singular Value Decomposition), a fast and memory efficient method for finding latent factors of time series data in an arbitrary time range. TR-SVD incrementally compresses multiple time series data block by block to reduce the space cost in storage phase, and efficiently computes singular value decomposition (SVD) for a given time range query in query phase by carefully stitching stored SVD results.
4. Propagation of content similarity through a collaborative network for live show recommendation
Jean Creusefond, Matthieu Latapy
We combine collaborative and content-based filtering to take benefit of past activity of users and of the features of the new show. Indeed, as this show is new we cannot rely on collaborative filtering only. To solve this cold-start problem, we perform network alignment and insert the new show in a way consistent with collaborative filtering.
5. A multi-level collaborative filtering method that improves recommendations
Nikolaos Polatidis, Christos K. Georgiadis
Collaborative filtering is one of the most used approaches for providing recommendations in various online environments. In this paper we propose a multi-level recommendation method with its main purpose being to assist users in decision making by providing recommendations of better quality. The proposed method can be applied in different online domains that use collaborative recommender systems, thus improving the overall user experience.
Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc or directly come from an explainable model (also called interpretable or transparent model in some context). Explainable recommendation tries to address the problem of why: by providing explanations to users or system designers, it helps humans to understand why certain items are recommended by the algorithm, where the human can either be users or system designers. Explainable recommendation helps to improve the transparency, persuasiveness, effectiveness, trustworthiness, and satisfaction of recommendation systems. In this survey, we review works on explainable recommendation in or before the year of 2019. We first highlight the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why. We then conduct a comprehensive survey of explainable recommendation on three perspectives: 1) We provide a chronological research timeline of explainable recommendation, including user study approaches in the early years and more recent model-based approaches. 2) We provide a two-dimensional taxonomy to classify existing explainable recommendation research: one dimension is the information source (or display style) of the explanations, and the other dimension is the algorithmic mechanism to generate explainable recommendations. 3) We summarize how explainable recommendation applies to different recommendation tasks, such as product recommendation, social recommendation, and POI recommendation. We also devote a section to discuss the explanation perspectives in broader IR and AI/ML research. We end the survey by discussing potential future directions to promote the explainable recommendation research area and beyond.
Images account for a significant part of user decisions in many application scenarios, such as product images in e-commerce, or user image posts in social networks. It is intuitive that user preferences on the visual patterns of image (e.g., hue, texture, color, etc) can be highly personalized, and this provides us with highly discriminative features to make personalized recommendations. Previous work that takes advantage of images for recommendation usually transforms the images into latent representation vectors, which are adopted by a recommendation component to assist personalized user/item profiling and recommendation. However, such vectors are hardly useful in terms of providing visual explanations to users about why a particular item is recommended, and thus weakens the explainability of recommendation systems. As a step towards explainable recommendation models, we propose visually explainable recommendation based on attentive neural networks to model the user attention on images, under the supervision of both implicit feedback and textual reviews. By this, we can not only provide recommendation results to the users, but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner. Experimental results show that our models are not only able to improve the recommendation performance, but also can provide persuasive visual explanations for the users to take the recommendations.