| 文献作者 | Aleksandr Farseev*, Ivan Samborskii** *, Andrey Filchenkov**, Tat-Seng Chua* | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2018-01-27 | ||||||||
| 文献关键字 | Grassmannn manifold; group knowledge; Spectral clustering; 谱聚类；SIGIR | ||||||||||
| 摘要描述 | Venue category recommendation is an essential application for the tourism and advertisement industries, wherein it may sug- gest attractive localities within close proximity to users’ current location. Considering that many adults use more than three so- cial networks simultaneously, it is reasonable to leverage on this rapidly growing multi-source social media data to boost venue rec- ommendation performance. Another approach to achieve higher recommendation results is to utilize group knowledge, which is able to diversify recommendation output. Taking into account these two aspects, we introduce a novel cross-network collaborative rec- ommendation framework C 3 R, which utilizes both individual and group knowledge, while being trained on data from multiple social media sources. Group knowledge is derived based on new cross- source user community detection approach, which utilizes both inter-source relationship and the ability of sources to complement each other. To fully utilize multi-source multi-view data, we pro- cess user-generated content by employing state-of-the-art text, im- age, and location processing techniques. Our experimental results demonstrate the superiority of our multi-source framework over state-of-the-art baselines and different data source combinations. In addition, we suggest a new approach for automatic construc- tion of inter-network relationship graph based on the data, which eliminates the necessity of having pre-defined domain knowledge. | ||||||||||
| 文献作者 | Sinno Jialin Pan and Qiang Yang | ||||||||||
| 文献发表年限 | 2010 | 创建时间 | 2018-01-27 | ||||||||
| 文献关键字 | Transfer learning, survey, machine learning, data mining. | ||||||||||
| 摘要描述 | A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. | ||||||||||
| 文献作者 | Julian McAuley; Jure Leskovec | ||||||||||
| 文献发表年限 | 2013 | 创建时间 | 2018-01-25 | ||||||||
| 文献关键字 | recommender systems, topic models, librec | ||||||||||
| 摘要描述 | In order to recommend products to users, we must ultimately predict how a user will respond to a new product. To do so we must uncover the implicit tastes of each user as well as the properties of each product. For example, in order to predict whether a user will enjoy Harry Potter, it helps to identify that the book is about wizards, as well as the user’s level of interest in wizardry. User feedback is required to discover these latent product and user dimensions. Such feedback often comes in the form of a numeric rating accompanied by review text. However, traditional methods often discard review text, which makes user and product latent dimensions difficult to interpret, since they ignore the very text that justifies a user’s rating. In this paper, we aim to combine latent rating dimensions (such as those of latent-factor recommender systems) with latent review topics (such as those learned by topic models like LDA). Our approach has several advantages. Firstly, we ob- tain highly interpretable textual labels for latent rating dimensions, which helps us to ‘justify’ ratings with text. Secondly, our approach more accurately predicts product ratings by harnessing the informa- tion present in review text; this is especially true for new products and users, who may have too few ratings to model their latent fac- tors, yet may still provide substantial information from the text of even a single review. Thirdly, our discovered topics can be used to facilitate other tasks such as automated genre discovery, and to identify useful and representative reviews. | ||||||||||
| 文献作者 | 杨芳洲； 曹健 | ||||||||||
| 文献发表年限 | 2015 | 创建时间 | 2018-01-14 | ||||||||
| 文献关键字 | 硕士毕业论文；机票个性化推荐,推荐算法,隐式反馈,协同过滤,选择模型 | ||||||||||
| 摘要描述 | 随着互联网的发展以及大数据时代的到来,信息爆炸所带来的信息过载的问题 也越发明显。推荐系统作为解决信息过载问题的一个有效解决方案,能够有效地为 用户个性化地推荐其感兴趣的产品与信息,其在过去数十年中也逐渐成为一个重要 的研究热点并被广泛地应用到工业领域。 论文主要研究推荐系统在机票个性化推荐问题中的应用。与传统的推荐系统的 推荐对象,如电影、书籍等具有相对固定属性的静态商品不同,机票是属于易受时 间影响的,且价格敏感的动态商品。同一张机票在距离起飞的不同时间有着较大的 价格波动,而不同的机票价格波动将直接影响用户的购买行为。 文中通过研究和分析用户的历史机票订单数据特征,提出了一种基于用户偏好 模型的机票个性化推荐算法,该方法类似于基于内容推荐的 KNN 的方法,并且引入 了信息熵的概念,用来计算用户在不同航线中各个机票特征上的个性化的偏好权重。 此外,针对用户在非活跃航线上数据稀疏的问题,还提出了基于航线的协同过滤的 算法,用来帮助用户进行跨航线的偏好学习。 此外,文中还提出了一种基于选择模型的机票个性化推荐算法。通过对用户在 机票历史订单的成对选择分析,来建立用户选择机票时的效用目标函数及其优化问 题。同时,还针对机票动态商品属性,提出了一种结合回归隐语义模型的效用函数 模型,其能够很好地结合航班的固定信息和机票本身的动态属性特征,通过隐式空 间特征更精准地刻画用户对机票的偏好。 最后,论文还结合了大数据技术,提出了一个面向大数据的机票个性化推荐系 统设计框架,包含了数据层,应用层,以及包含了一个离线计算单元和一个在线计 算单元的推荐逻辑层。同时,还对文中提出的两种机票个性化推荐算法进行了基于 Spark 并行计算框架的并行化设计与实现。 | ||||||||||
| 文献作者 | Yongfeng Zhang | ||||||||||
| 文献发表年限 | 2014 | 创建时间 | 2018-01-11 | ||||||||
| 文献关键字 | Recommender Systems; Sentiment Analysis; Collaborative Filtering; Recommendation Explanation; EFM | ||||||||||
| 摘要描述 | Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, with the continuous growth of online user re- views, the information available for training a recommender system is no longer limited to just numerical star ratings or user/item features. By extracting explicit user opinions about various aspects of a product from the reviews, it is possible to learn more details about what aspects a user cares, which further sheds light on the possibility to make explainable recommendations. In this work, we propose the Explicit Factor Model (EFM) to generate explainable recommendations, meanwhile keep a high prediction accuracy. We first extract explicit product features (i.e. aspects) and user opinions by phrase-level sen- timent analysis on user reviews, then generate both recommendations and disrecommendations according to the spe- cific product features to the user’s interests and the hid- den features learned. Besides, intuitional feature-level ex- planations about why an item is or is not recommended are generated from the model. Offline experimental results on several real-world datasets demonstrate the advantages of our framework over competitive baseline algorithms on both rating prediction and top-K recommendation tasks. Online experiments show that the detailed explanations make the recommendations and disrecommendations more influential on user’s purchasing behavior. | ||||||||||
| 文献作者 | Hyun-seok Kim | ||||||||||
| 文献发表年限 | 2016 | 创建时间 | 2017-12-28 | ||||||||
| 文献关键字 | 21gene 预测；预测模型中的假设检验；乳腺癌症；医学机器学习 | ||||||||||
| 摘要描述 | Gene expression profiling assays are frequently used to guide adjuvant chemotherapy decisions in hormone receptor–positive, lymph node–negative breast cancer. We hypothesized that the clinical value of these new tools would be more fully realized when appropriately integrated with high-quality clinicopathologic data. Hence, we developed a model that uses routine pathologic parameters to estimate Oncotype DX recurrence score (ODX RS) and independently tested its ability to predict ODX RS in clinical samples. | ||||||||||
| 文献作者 | Leihui Chen, Jianbing Zheng | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2017-12-17 | ||||||||
| 文献关键字 | transfer learning; cross-domain; partially overlapped | ||||||||||
| 摘要描述 | In the era of big data, the available information on the Internet has overwhelmed the human processing capabilities in some commercial applications. Recommendation techniques are indispensable to predict user ratings on items in terms of historical data and deal with the information overload. In many applications, the problem of data sparsity usually results in overfitting and fails to give desirable performance. Therefore, many works have started to investigate the techniques of cross- domain recommendation to overcome the challenge. However, it is not trivial. In this paper, we propose a transfer learning algorithm, named TLRec, for cross-domain recommendation, which exploits the overlapped users and items as a bridge to link different domains and implements knowledge transfer. We learn parameters based on the defined empirical prediction error, smoothness and regularization of user and item latent vectors. We also establish a relation between TLRec and vertex vectoring on bipartite graphs. The experimental result illustrates that TLRec has promising performance and outperforms several state-of-the- art approaches on a real dataset. | ||||||||||
| 文献作者 | Shatha Jaradat | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2017-12-14 | ||||||||
| 文献关键字 | Fashion recommendation; deep learning; cross-domain knowledge transfer; transfer learning; domain adaptation; CNN | ||||||||||
| 摘要描述 | With the increasing number of online shopping services, the number of users and the quantity of visual and textual information on the Internet, there is a pressing need for intelligent recommendation systems that analyze the user’s behavior amongst multiple domains and help them to find the desirable information without the burden of search. However, there is little research that has been done on complex recommendation scenarios that involve knowledge transfer across multiple domains. This problem is especially challenging when the involved data sources are complex in terms of the limitations on the quantity and quality of data that can be crawled. The goal of this paper is studying the connection between visual and textual inputs for better analysis of a certain domain, and to examine the possibility of knowledge transfer from complex domains for the purpose of efficient recommendations. The methods employed to achieve this study include both design of architecture and algorithms using deep learning technologies to analyze the effect of deep pixel-wise semantic segmentation and text integration on the quality of recommendations. We plan to develop a practical testing environment in a fashion domain. | ||||||||||
| 文献作者 | Shiyou Qian; Jian Cao | ||||||||||
| 文献发表年限 | 2015 | 创建时间 | 2017-12-13 | ||||||||
| 文献关键字 | Recommender Systems; Assignment Mechanism; Fairness; Taxis | ||||||||||
| 摘要描述 | Recommending routes for a group of competing taxi drivers is almost untouched in most route recommender systems. For this kind of problem, recommendation fairness and driving efficiency are two fundamental aspects. In the paper, we propose SCRAM, a sharing considered route assignment mechanism for fair taxi route recommendations. SCRAM aims to provide recommendation fairness for a group of competing taxi drivers, without sacrificing driving efficiency. By designing a concise route assignment mechanism, SCRAM achieves better recommendation fairness for competing taxis. By considering the sharing of road sections to avoid unnecessary competition, SCRAM is more efficient in terms of driving cost per customer (DCC). We test SCRAM based on a large number of historical taxi trajectories and validate the recommendation fairness and driving efficiency of SCRAM with extensive evaluations. Experimental results show that SCRAM achieves better recommendation fairness and higher driving effi- ciency than three compared approaches. | ||||||||||
