Context-Aware Computational Trust Model for Recommender Systems
With increase in computing and networking technologies, many organizations have managed to place their services online with the aim of achieving efficiency in customer service as well as reach more potential customers, also with communicable diseases such as COVID-19 and need for social distancing, many people are encouraged to work from home, including shopping. To meet this objective in areas with poor Internet connectivity, the government of Kenya recently announced partnership with Google Inc for use of Google Loon. This has come up with challenges which include information overload on the side of the end consumer as well as security loopholes such as dishonest vendors preying on unsuspecting consumers. Recommender systems have been used to alleviate these two challenges by helping online users select the best item for their case. However, most recommender systems, especially common filtering recommendation algorithm (CFRA) based systems still rely on presenting output based on selections of nearest neighbors (most similar users – birds of the same feathers flock together). This leaves room for manipulation of the output by mimicking the features of their target and then picking malicious item such that when the recommender system runs, it will output the same malicious item to the target – a trust issue. Data to construct trust is equally a challenge. In this research, we propose to address this issue by creating a trust adjustment factor (TAF) for recommender systems for online services.
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