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首页> 外文期刊>Indian Journal of Science and Technology >A Recommender System for Improved Web Usage Mining and Personalization based on Foraging behavior based Swarm Intelligence
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A Recommender System for Improved Web Usage Mining and Personalization based on Foraging behavior based Swarm Intelligence

机译：基于觅食行为群智能的Web挖掘和个性化推荐系统

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Objectives: The core intent of this paper is to propose a dynamic recommender system(i) to endow with an enhanced understanding of the behaviors and interest of online users and (ii) to tumble information overload by providing guidance to the online users. Methods: Foraging behavior based swarm intelligence is motivated from the dynamic social behavior of swarms. It puts forward innate option for modeling dynamic online usage data. In the preset work, we mainly concentrate on the innate resemblance between Swarm Intelligence and communal behavior. The proposed WebFrieseBee algorithm is inspired from the foraging behavior of the T. biroiFriese bees and the proposed the proposed BestProphecy-annealing algorithm is used for providing recommendations to users. Findings: The proposed WebFrieseBee is motivated from the dynamic social behavior of swarms. It offers collaborative learning and has decentralized control. Moreover, it also has high exploration ability. That is, it put forward innate option for modeling dynamic online usage data. The proposed BestProphecy-annealing algorithm uses a greedy heuristic approach, for providing recommendations to users by identifying a better neighborhood for agents and gives recommendations based on the preferences of these best neighborhoods. The proposed WebFrieseBee may overcome the data redundancies existing in the repeated use of information which are inappropriate and may provide tradeoff between coverage and precision. Improvements: Our proposed dynamic recommender system surmount the grey sheep problem and new user ramp up problem in many traditional recommender systems. Our proposed dynamic recommender system was compared with Ant clustering approach. The Experimental results shows that our approach offers better quality in terms of coverage, precision and F1 Measure than the traditional Ant clustering approaches. Applications: The recommender system is very effective in increasing the utility of e-commerce by minimizing the user surfing time and overload in servers.
机译：目标：本文的核心目的是提出一种动态推荐系统（i）以增强对在线用户的行为和兴趣的理解，以及（ii）通过为在线用户提供指导来缓解信息过载。方法：基于群的动态社交行为来激发基于群行为的觅食行为。提出了用于动态在线使用数据建模的先天选项。在预设工作中，我们主要关注群体智能与群体行为之间的先天相似性。拟议的WebFrieseBee算法是从T. biroiFriese蜜蜂的觅食行为中得到启发的，而拟议的BestProphecy退火算法则用于向用户提供建议。结果：提议的WebFrieseBee受到群体动态社会行为的启发。它提供协作学习，并具有分散的控制权。而且，它还具有很高的勘探能力。也就是说，它提出了用于动态在线使用数据建模的先天选项。提出的BestProphecy退火算法使用贪婪启发式方法，通过为代理标识更好的邻域来向用户提供建议，并根据这些最佳邻域的偏好给出建议。提出的WebFrieseBee可以克服信息重复使用中存在的数据冗余，这种冗余是不合适的，并且可以在覆盖范围和精度之间进行权衡。改进：我们提出的动态推荐器系统克服了许多传统推荐器系统中的灰羊问题和新用户增加问题。我们提出的动态推荐系统与蚂蚁聚类方法进行了比较。实验结果表明，与传统的蚂蚁聚类方法相比，我们的方法在覆盖范围，精度和F1度量方面提供了更好的质量。应用程序：推荐器系统通过最小化用户的冲浪时间和服务器的过载，在提高电子商务的效用方面非常有效。

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