基于行为-内容融合模型的用户画像研究

[目的/意义]为识别并去除非理性投资者的网络评论,提升评论的专业程度与质量,促进理性投资,本文以识别股吧中的用户是否属于噪声投资者为研究任务,进行用户画像。[方法/过程]对股吧的用户发文内容进行深度用户表示学习（deep user representation learning）,结合股吧用户的粉丝数量、影响力、关注量、自选股、吧龄、发帖量、评论量、访问量等行为特征,提出一种行为-内容融合模型（behaviour and content combined model,BCCM）,并在标注数据集上进行实证与对比研究。[结果/结论]实验结果显示,该模型对噪声投资者识别的F1值为79.47%,优于决策树方法（69.90%）、SVM方法（75.61%）、KNN方法（73.21%）和ANN方法（74.83%）。在噪声投资者识别这一特定用户画像研究任务中,通过利用深度用户表示学习引入文本内容特征,能够显著提升用户画像的各种评价指标。     

A study of untrained models for multimodal information retrieval

Operational multimodal information retrieval systems have to deal with increasingly complex document collections and queries that are composed of a large set of textual and non-textual modalities such as ratings, prices, timestamps, geographical coordinates, etc. The resulting combinatorial explosion of modality combinations makes it intractable to treat each modality individually and to obtain suitable training data. As a consequence, instead of finding and training new models for each individual modality or combination of modalities, it is crucial to establish unified models, and fuse their outputs in a robust way. Since the most popular weighting schemes for textual retrieval have in the past generalized well to many retrieval tasks, we demonstrate how they can be adapted to be used with non-textual modalities, which is a first step towards finding such a unified model. We demonstrate that the popular weighting scheme BM25 is suitable to be used for multimodal IR systems and analyze the underlying assumptions of the BM25 formula with respect to merging modalities under the so-called raw-score merging hypothesis, which requires no training. We establish a multimodal baseline for two multimodal test collections, show how modalities differ with respect to their contribution to relevance and the difficulty of treating modalities with overlapping information. Our experiments demonstrate that our multimodal baseline with no training achieves a significantly higher retrieval effectiveness than using just the textual modality for the social book search 2016 collection and lies in the range of a trained multimodal approach using the optimal linear combination of the modality scores.     

A language model-based framework for multi-publisher content-based recommender systems

The rapid growth of the Web has increased the difficulty of finding the information that can address the users’ information needs. A number of recommendation approaches have been developed to tackle this problem. The increase in the number of data providers has necessitated the development of multi-publisher recommender systems; systems that include more than one item/data provider. In such environments, preserving the privacy of both publishers and subscribers is a key and challenging point. In this paper, we propose a multi-publisher framework for recommender systems based on a client–server architecture, which preserves the privacy of both data providers and subscribers. We develop our framework as a content-based filtering system using the statistical language modeling framework. We also introduce AUTO, a simple yet effective threshold optimization algorithm, to find a dissemination threshold for making acceptance and rejection decisions for new published documents. We further propose a language model sketching technique to reduce the network traffic between servers and clients in the proposed framework. Extensive experiments using the TREC-9 Filtering Track and the CLEF 2008-09 INFILE Track collections indicate the effectiveness of the proposed models in both single- and multi-publisher settings.     

Neural information retrieval: at the end of the early years

A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning. Recent years have witnessed an explosive growth of research into NN-based approaches to information retrieval (IR). A significant body of work has now been created. In this paper, we survey the current landscape of Neural IR research, paying special attention to the use of learned distributed representations of textual units. We highlight the successes of neural IR thus far, catalog obstacles to its wider adoption, and suggest potentially promising directions for future research.     

公安派"性灵说"今辨

“性灵说”是公安派的核心观点,对打破前后七子复古樊篱起到了重要作用,同时也对后世的文学理论发展影响深远。通过概述其内涵及审美特征,辨析各家批评之说,指出“性灵说”的精华对今人创作仍有重要借鉴意义。     

关于高阶中立型非线性时滞微分方程振动及非振动性一文的一点注记

实质性地改进了“高阶中立型非线性时滞微分方程振动及非振动性一文中定理的主要条件。     

美国高等教育体制浅探

美国是世界上教育事业最发达的国家之一,高等教育已经普及。探索美国高等教育体制,分析大学对美国发展的重要贡献,或许能为我国高等教育发展有所借鉴。