Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation system (Iter_ALS Iterative Alternate Least Square) to enhance the recommendation accuracy by integrating rating and emotion tags. The rating score reveals overall perception of the item and emotion tags reflects user’s feelings. In the absence of emotional tags, scores found in rating is assumed as positive or negative emotional tag score. Lexicon based semantic analysis on emotion tags value is adopted to represent the exclusive value of tag. Unified value is represented into Iter_ALS model to reduce the sparsity problem. In addition, this method handles opinion bias between ratings and tags. Experiments were tested and verified using a benchmark project of MovieLens dataset. Initially this model was tested with different sparsity levels varied between 0%-100 percent and the results obtained from the experiments shows the proposed method outperforms with baseline methods. Further tests were conducted to authenticate how it handles opinion bias by users before recommending the item. The proposed method is more capable to be adopted in many real world applications

中文翻译：

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大数据环境下混合协同过滤方法的情感分析

大多数传统的推荐系统都是基于用户评分的。在这里，用户在使用或体验产品后提供对产品的评分。因此，构建用户项目事务数据库用于推荐。基于评分的协同过滤方法是推荐系统中众所周知的方法。该系统导致数据稀疏问题，因为用户不知道其他类似项目。诸如标签之类的 Web 编目服务在分析用户对特定产品的看法方面发挥着重要作用。一些系统使用标签作为附加资源来减少数据稀疏问题。但是这些系统需要很多与标签相关的具体细节。现有系统要么侧重于评级，要么侧重于基于标签的推荐，以提高准确性。因此，这些系统存在数据稀疏性和效率问题，导致推荐准确性低下。针对上述问题，本文提出了混合推荐系统（Iter_ALS Iterative Alternate Least Square），通过整合评分和情感标签来提高推荐准确性。评级分数揭示了项目的整体感知，情感标签反映了用户的感受。在没有情感标签的情况下，评分中的分数被假定为积极或消极的情感标签分数。采用基于词典的情感标签值语义分析来表示标签的独占值。统一值表示为 Iter_ALS 模型以减少稀疏问题。此外，该方法还可以处理评分和标签之间的意见偏差。使用 MovieLens 数据集的基准项目对实验进行了测试和验证。最初，该模型在 0%-100% 之间变化的不同稀疏度水平下进行了测试，从实验中获得的结果表明，所提出的方法优于基线方法。在推荐项目之前，进行了进一步的测试以验证它如何处理用户的意见偏见。所提出的方法更能够在许多实际应用中采用