智能教育下基于情境感知的自适应学习系统构建

伴随着大数据和人工智能在教育领域的渗透和融合,个性化学习成为当前和未来教育关注的焦点,而自适应学习系统为个性化学习提供了一种实践路径.在分析自适应学习基本模型的基础上,结合自适应超媒体系统通用模型AEHS,引入了学习情境,构建了基于情境感知的自适应学习系统模型.为了提高自适应结果的精准性,根据学习情境中的学习者要素、时间要素、空间要素和设备要素,对学习者进行学习情境分组,以此为基础,通过学习者模型、领域模型和教学模型的协同作用,在自适应引擎的驱动下,生成自适应学习结果.最后,结合动态变化的时间因素,提供了个性化资源推荐的实现思路.     

利用RFID技术实现图书馆个性化推荐探讨

基于不断丰富的图书资源与读者找书难之间的日益突出的矛盾,简述了RFID技术与图书馆个性化推荐系统的国内外研究现状,分析了移动环境条件下图书馆推荐服务方式的转变,在此基础上,探讨了利用RFID技术实现图书馆用户定位的个性化推荐服务模式的可行性。     

学校体育面临的困境与思路对策

长期以来,受升学考试制度的影响,我国学校体育一直处于被边缘化和淡化的境地。文章在分析学校体育面临的诸多困境的基础上,就加强学校体育提出一些基本思路及对策,以加强学校体育,强健学生体魄,增强学生综合素质。     

Combining Non-sampling and Self-attention for Sequential Recommendation

With the rapid development of social media and big data technology, user’s sequence behavior information can be well recorded and preserved on different media platforms. It is crucial to model the user preference through mining their sequential behaviors. The goal of sequential recommendation is to predict what a user may interact with in the next moment based on the user’s historical record of interactive sequence. However, existing sequential recommendation methods generally adopt a negative sampling mechanism (e.g. random and uniform sampling) for the pairwise learning, which brings the defect of insufficient training to the model, and decrease the evaluation performance of the entire model. Therefore, we propose a Non-sampling Self-attentive Sequential Recommendation (NSSR) model that combines non-sampling mechanism and self-attention mechanism. Under the premise of ensuring the efficient training of the model, NSSR model takes all pairs in the training set as training samples, so as to achieve the goal of fully training the model. Specifically, we take the interactive sequence as the current user representation, and propose a new loss function to implement the non-sampling training mechanism. Finally, the state-of-the-art result is achieved on three public datasets, Movielens-1M, Amazon Beauty and Foursquare_TKY, and the recommendation performance increase by about 29.3%, 25.7% and 42.1% respectively.     

Dynamic intent-aware iterative denoising network for session-based recommendation

Session-based recommendation aims to predict items that a user will interact with based on historical behaviors in anonymous sessions. It has long faced two challenges: (1) the dynamic change of user intents which makes user preferences towards items change over time; (2) the uncertainty of user behaviors which adds noise to hinder precise preference learning. They jointly preclude recommender system from capturing real intents of users. Existing methods have not properly solved these problems since they either ignore many useful factors like the temporal information when building item embeddings, or do not explicitly filter out noisy clicks in sessions. To tackle above issues, we propose a novel Dynamic Intent-aware Iterative Denoising Network (DIDN) for session-based recommendation. Specifically, to model the dynamic intents of users, we present a dynamic intent-aware module that incorporates item-aware, user-aware and temporal-aware information to learn dynamic item embeddings. A novel iterative denoising module is then devised to explicitly filter out noisy clicks within a session. In addition, we mine collaborative information to further enrich the session semantics. Extensive experimental results on three real-world datasets demonstrate the effectiveness of the proposed DIDN. Specifically, DIDN obtains improvements over the best baselines by 1.66%, 1.75%, and 7.76% in terms of P@20 and 1.70%, 2.20%, and 10.48% in terms of MRR@20 on all datasets.     

Leveraging social influence based on users activity centers for point-of-interest recommendation

Recommender Systems (RSs) aim to model and predict the user preference while interacting with items, such as Points of Interest (POIs). These systems face several challenges, such as data sparsity, limiting their effectiveness. In this paper, we address this problem by incorporating social, geographical, and temporal information into the Matrix Factorization (MF) technique. To this end, we model social influence based on two factors: similarities between users in terms of common check-ins and the friendships between them. We introduce two levels of friendship based on explicit friendship networks and high check-in overlap between users. We base our friendship algorithm on users’ geographical activity centers. The results show that our proposed model outperforms the state-of-the-art on two real-world datasets. More specifically, our ablation study shows that the social model improves the performance of our proposed POI recommendation system by 31% and 14% on the Gowalla and Yelp datasets in terms of Precision@10, respectively.     

社会资本视域下乡镇党委公推直选发展探析

公推直选制度是党内民主建设和乡村社会建设的重要制度创新,而社会资本代表社会结构和社会文化中个人存在于社会的公共理性,是社会发展的基石,对公推直选的存续具有较强的支撑价值。为此,从社会关系网络、社会道德和信任态度三个维度来审视产生于熟人半熟人社会、易受道德评价影响的而又非常依赖信任品质的乡镇公推直选,探讨在乡镇公推直选中社会资本利用的优势与困境,进而尝试有指向地挖掘社会资本以促进公推直选制度的深入发展。     

一种改进协同过滤推荐算法

在传统的协同过滤推荐算法基础上,对传统协同过滤算法中冷门物品不能进行处理问题进行剖析,提出一种改进的协同过滤推荐算法。通过实验仿真,验证了该算法的有效性。     

基于微信公众号的高校图书馆图书共享服务系统的设计与实现

论文基于高校图书馆服务系统OPAC的使用现状的调查,针对该系统在使用过程中存在的一些问题和不足,利用微信公众平台的优势,提出了互动性良好的综合解决方案,设计了基于微信公众号的高校图书馆图书共享服务系统。实践证明,系统能够使图书馆更好地为读者服务,大大提高了图书馆服务水平,实现了高校图书馆与用户的良性互动,构建了以书为媒介的生态环境。     

基于用户画像的高校图书馆个性化资源推荐服务设计

用户画像作为大数据分析背景下个性化推荐服务的设计工具,为高校图书馆领域个性化阅读资源推荐服务提供解决思路。本研究在分析目前个性化推荐和用户画像研究的基础上,引入用户画像技术,从数据基础层、数据处理层、画像构建层、画像服务层设计探讨用户画像的构建流程,重点在用户画像构建和画像服务层面进行阐述,同时从用户基本属性、阅读状态、学习风格、阅读偏好四个维度构建用户多维画像模型,并提出基于冷启动和用户阅读学习过程画像的个性化推荐服务策略,以期为后疫情教育环境下高校图书馆开展个性化资源推荐服务和满足用户多维度阅读学习需求提供参考。