Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking

This paper proposes a new neural architecture for collaborative ranking with implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning}) is a novel metric learning approach for recommendation. More specifically, instead of simple push-pull mechanisms between user and item pairs, we propose to learn latent relations that describe each user item interaction. This helps to alleviate the potential geometric inflexibility of existing metric learing approaches. This enables not only better performance but also a greater extent of modeling capability, allowing our model to scale to a larger number of interactions. In order to do so, we employ a augmented memory module and learn to attend over these memory blocks to construct latent relations. The memory-based attention module is controlled by the user-item interaction, making the learned relation vector specific to each user-item pair. Hence, this can be interpreted as learning an exclusive and optimal relational translation for each user-item interaction. The proposed architecture demonstrates the state-of-the-art performance across multiple recommendation benchmarks. LRML outperforms other metric learning models by $6\%-7.5\%$ in terms of Hits@10 and nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover, qualitative studies also demonstrate evidence that our proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback. As such, this ascertains the ability of LRML to uncover hidden relational structure within implicit datasets.Comment: WWW 201

SAVE-SD 2017: Third Workshop on Semantics, Analytics and Visualisation: Enhancing Scholarly Data

The third edition of the Workshop on Semantics, Analytics and Visualisation: Enhancing Scholarly Data (SAVE-SD 2017) is taking place in Perth, Australia on the 3rd of April 2017, co-located with the 26th International World Wide Web Conference. The main goal of the workshop is to provide a venue for researchers, publishers and other companies to engage in discussions about semantics, analytics and visualisations on scholarly data

Explaining Latent Factor Models for Recommendation with Influence Functions

Latent factor models (LFMs) such as matrix factorization achieve the state-of-the-art performance among various Collaborative Filtering (CF) approaches for recommendation. Despite the high recommendation accuracy of LFMs, a critical issue to be resolved is the lack of explainability. Extensive efforts have been made in the literature to incorporate explainability into LFMs. However, they either rely on auxiliary information which may not be available in practice, or fail to provide easy-to-understand explanations. In this paper, we propose a fast influence analysis method named FIA, which successfully enforces explicit neighbor-style explanations to LFMs with the technique of influence functions stemmed from robust statistics. We first describe how to employ influence functions to LFMs to deliver neighbor-style explanations. Then we develop a novel influence computation algorithm for matrix factorization with high efficiency. We further extend it to the more general neural collaborative filtering and introduce an approximation algorithm to accelerate influence analysis over neural network models. Experimental results on real datasets demonstrate the correctness, efficiency and usefulness of our proposed method

A Semantic-enabled Framework For Future Internet Of Things Applications

While the challenge of connecting Internet of Things (IoT) devices at the lowest layer has been widely studied, integrating and interoperating huge amounts of sensed data of heterogeneous IoT devices is becoming increasingly important because of the possibility of consuming such data in supporting many potential novel IoT applications. A common approach to processing and consuming IoT data is a centralized paradigm: sensor data is sent over the network to a comparatively powerful central server or a cloud service, where all processing takes place. However, this approach has some limitations as it requires devices to interact directly with a cloud which is not cost effective. First, it has high demands on the device's storage and computational capabilities. Second, as devices grow rapidly in a deployment area, sending all the data to a centralized cloud server requires high network bandwidth. Moreover, this often creates data privacy concerns as all raw data will be sent to a centralized place. To address the above limitations for building future Internet of Things applications, we present an early design of a novel framework that combines Internet of Things, Semantic Web, and Big Data concepts. We not only present the core components to build an IoT system, but also list existing alternatives with their merits. This framework aims to incorporate open standards to address the potential challenges in building future IoT applications. Therefore, our discussion revolves around open standards to build the framework, rather than proprietary standards