| 文献作者 | Chen Lin | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-04-12 | ||||||||
| 文献关键字 | Bradley-Terry Model; 偏好结构；decision procesison； 非线性加权 | ||||||||||
| 摘要描述 | The study of consumer psychology reveals two categories of consumption decision procedures: compensatory rules and non-compensatory rules. Existing recommendation models which are based on latent factor models assume the consumers follow the compensatory rules, i.e. they evaluate an item over multiple aspects and compute a weighted or/and summated score which is used to derive the rating or ranking of the item. However, it has been shown in the literature of consumer behavior that, consumers adopt non-compensatory rules more often than compensatory rules. Our main contribution in this paper is to study the unexplored area of utilizing non-compensatory rules in recommendation models. Our general assumptions are (1) there are K universal hid- den aspects. In each evaluation session, only one aspect is chosen as the prominent aspect according to user preference. (2) Evaluations over prominent and non-prominent aspects are non-compensatory. Evaluation is mainly based on item performance on the prominent aspect. For non-prominent aspects the user sets a minimal acceptable threshold. We give a conceptual model for these general assumptions. We show how this conceptual model can be realized in both pointwise rating prediction models and pair-wise ranking prediction models. Experiments on real-world data sets validate that adopting non-compensatory rules improves recommendation performance for both rating and ranking models. | ||||||||||
| 文献作者 | Trong Dinh Thac Do | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-12 | ||||||||
| 文献关键字 | Poisson Factorization; content-based + CF; Gamma; graphical models; VI (Variational Inference)；属性集合 | ||||||||||
| 摘要描述 | Matrix Factorization (MF) is widely used in Recommender Systems (RSs) for estimating missing ratings in the rating matrix. MF faces major challenges of handling very sparse and large data. Poisson Factorization (PF) as an MF variant addresses these challenges with high efficiency by only computing on those non-missing elements. However, ignoring the missing elements in computation makes PF weak or incapable for dealing with columns or rows with very few observations (corresponding to sparse items or users). In this work, Metadata-dependent Poisson Factorization (MPF) is invented to address the user/item sparsity by integrating user/item metadata into PF. MPF adds the metadata-based observed entries to the factorized PF matrices. In addition, similar to MF, choosing the suitable number of latent components for PF is very expensive on very large datasets. Accordingly, we further extend MPF to Metadata-dependent Infinite Poisson Factorization (MIPF) that integrates Bayesian Nonparametric (BNP) technique to automatically tune the number of latent components. Our empirical results show that, by integrating metadata, MPF/MIPF sig- nificantly outperform the state-of-the-art PF models for sparse and large datasets. MIPF also effectively estimates the number of latent components. | ||||||||||
| 文献作者 | Buru Chang | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-11 | ||||||||
| 文献关键字 | embedding learning; word2vec; content based; POI; skip-gram； 初始化隐向量 | ||||||||||
| 摘要描述 | Recommending a point-of-interest (POI) a user will visit next based on temporal and spatial context information is an important task in mobile-based applications. Recently, several POI recommendation models based on conventional sequential-data modeling approaches have been proposed. However, such models focus on only a user’s check-in sequence information and the physical distance between POIs. Furthermore, they do not utilize the characteristics of POIs or the relationships between POIs. To address this problem, we propose CAPE, the first content-aware POI embedding model which utilizes text content that provides information about the characteristics of a POI. CAPE consists of a check-in context layer and a text content layer. The check-in context layer captures the geographical influence of POIs from the check-in sequence of a user, while the text content layer captures the characteristics of POIs from the text content. To validate the efficacy of CAPE, we constructed a large-scale POI dataset. In the experimental evaluation, we show that the performance of the existing POI recommendation models can be significantly improved by simply applying CAPE to the models. | ||||||||||
| 文献作者 | Duc-Trong Le, Hady W. Lauw and Yuan Fang | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-10 | ||||||||
| 文献关键字 | LSTM; Cross domain; next-item; Siamese networks | ||||||||||
| 摘要描述 | Our interactions with an application frequently leave a heterogeneous and contemporaneous trail of actions and adoptions (e.g., clicks, bookmarks, pur- chases). Given a sequence of a particular type (e.g., purchases)– referred to as the target sequence, we seek to predict the next item expected to appear be- yond this sequence. This task is known as next-item recommendation. We hypothesize two means for improvement. First, within each time step, a user may interact with multiple items (a basket), with potential latent associations among them. Second, predicting the next item in the target sequence may be helped by also learning from another support- ing sequence (e.g., clicks). We develop three twin network structures modeling the generation of both target and support basket sequences. One based on “Siamese networks” facilitates full sharing of parameters between the two sequence types. The other two based on “fraternal networks” facilitate partial sharing of parameters. Experiments on real-world datasets show significant improvements upon baselines relying on one sequence type. | ||||||||||
| 文献作者 | Yang Zhao | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-10 | ||||||||
| 文献关键字 | stacked LSTM; 规则结合ML； | ||||||||||
| 摘要描述 | Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently. However, several studies indicate that NMT often generates fluent but unfaithful translations. In this paper, we propose a method to alleviate this problem by using a phrase table as recommendation memory. The main idea is to add bonus to words worthy of recommendation, so that NMT can make correct predictions. Specifically, we first derive a prefix tree to accommodate all the candidate target phrases by searching the phrase translation table according to the source sentence. Then, we construct a recommendation word set by matching between candidate target phrases and previously translated target words by NMT. After that, we determine the specific bonus value for each recommendable word by using the attention vector and phrase translation probability. Finally, we integrate this bonus value into NMT to improve the translation results. The extensive experiments demonstrate that the proposed methods obtain remarkable improvements over the strong attention- based NMT. | ||||||||||
| 文献作者 | Han Liu; Xiangnan He | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-10 | ||||||||
| 文献关键字 | FM; discrete; content-based | ||||||||||
| 摘要描述 | User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 107, results in expensive storage and computational cost. This prohibits fast recommendation especially on mobile applications where the computational resource is very limited. In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation. DFM binarizes the real-valued model param- eters (e.g., float32) of every feature embedding into binary codes (e.g., boolean), and thus supports effi- cient storage and fast user-item score computation. To avoid the severe quantization loss of the binarization, we propose a convergent updating rule that resolves the challenging discrete optimization of DFM. Through extensive experiments on two real-world datasets, we show that 1) DFM consistently outperforms state-of-the-art binarized recommendation models, and 2) DFM shows very competitive performance compared to its real-valued version (FM), demonstrating the minimized quantization loss. | ||||||||||
| 文献作者 | Quangui Zhang; Longbing Cao | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-09 | ||||||||
| 文献关键字 | non-IID; content based; CF based; CNN; NCF | ||||||||||
| 摘要描述 | Non-IID recommender system discloses the nature of recommendation and has shown its potential in improving recommendation quality and addressing issues such as sparsity and cold start. It leverages existing work that usually treats users/items as independent while ignoring the rich couplings within and between users and items, leading to limited performance improvement. In reality, users/items are related with various couplings existing within and between users and items, which may better explain how and why a user has personalized preference on an item. This work builds on non-IID learning to propose a neural user-item coupling learning for collaborative filtering, called CoupledCF. CoupledCF jointly learns explicit and implicit couplings within/between users and items w.r.t. user/item attributes and deep features for deep CF recommendation. Empirical results on two real-world large datasets show that CoupledCF significantly outperforms two latest neural recommenders: neural matrix factorization and Google’s Wide&Deep network. | ||||||||||
| 文献作者 | Antonio Rago, Oana Cocarascu, Francesca Toni | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-08 | ||||||||
| 文献关键字 | interpretation; A-I model; aspect-item model；可解释性；memory based; memory-based；公式很漂亮 | ||||||||||
| 摘要描述 | A significant problem of recommender systems is their inability to explain recommendations, resulting in turn in ineffective feedback from users and the inability to adapt to users’ preferences. We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with users’ partially given ratings, that can be naturally used to provide explanations for recommendations, extracted from user-tailored Tripolar Argumentation Frameworks (TFs). We show that our method can be understood as a gradual semantics for TFs, exhibiting a desirable, albeit weak, property of balance. We also show experimentally that our method is competitive in generating correct predictions, compared with state-of-the-art methods, and illustrate how users can interact with the generated explanations to improve quality of recommendations. | ||||||||||
| 文献作者 | Li Gao | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-08 | ||||||||
| 文献关键字 | point-wise recommendation; 多信息融合； random walk | ||||||||||
| 摘要描述 | Network embedding has been recently used in social network recommendations by embedding low- dimensional representations of network items for recommendation. However, existing item recommendation models in social networks suffer from two limitations. First, these models partially use item information and mostly ignore important contextual information in social networks such as textual content and social tag information. Second, network embedding and item recommendations are learned in two independent steps without any interaction. To this end, we in this paper consider item recommendations based on heterogeneous information sources. Specifically, we combine item structure, textual content and tag information for recommendation. To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source Deep Network Embedding (CDNE for short) is proposed to learn different latent representations. Experimental results on two real-world data sets demonstrate that CDNE can use network representation learning to boost the recommendation performance. | ||||||||||
| 文献作者 | Dilruk Perera and Roger Zimmermann | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-07 | ||||||||
| 文献关键字 | Cross-domain; LSTM: attention model; next-item recommendation | ||||||||||
| 摘要描述 | Cross-network recommender systems use auxiliary information from multiple source networks to create holistic user profiles and improve recommendations in a target network. However, we find two major limitations in existing cross-network solutions that reduce overall recommender performance. Existing models (1) fail to capture complex non-linear relationships in user interactions, and (2) are designed for offline settings hence, not updated online with incoming interactions to capture the dynamics in the recommender environment. We propose a novel multi-layered Long Short-Term Memory (LSTM) network based online solution to mitigate these issues. The proposed model contains three main extensions to the standard LSTM: First, an attention gated mechanism to capture long-term user preference changes. Second, a higher order interaction layer to alleviate data sparsity. Third, time aware LSTM cell gates to capture irregular time intervals between user interactions. We illustrate our solution using auxiliary information from Twitter and Google Plus to improve recommendations on YouTube. Extensive experiments show that the proposed model consistently outperforms state-of-the-art in terms of accuracy, diversity and novelty. | ||||||||||
