Abstract
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.
Highlights
Over the years there have been significant advances in Information and Communication Technologies (ICT) resulting in increased availability of high speed, reliable broadband internet services
Possibility of Incorporating Trust into Recommender Systems Even though trust seems an amorphous construct that cannot be physically measured, a scientific paper [18], has shown that if it is incorporated into a recommender system it improves the most desirable property of recommender system – the accuracy as measured by mean absolute error (MAE) and Root Means Square Error (RMSE)
Preliminary results show that of the respondents so far, they are aged between 18 -55 years, 56% male and 44 percent female, ranging between diploma students to PhD holders
Summary
Over the years there have been significant advances in Information and Communication Technologies (ICT) resulting in increased availability of high speed, reliable broadband internet services. Social distancing has resulted in pushing of a significant number of traditional services to the online space These include learning, shopping, medical consultation, gamming, hotel and restaurant services and even other forms of entertainment such as gaming and gambling. This has led to distrust of online and recommender systems in general since they are susceptible to manipulation This distrust does deny the online users the advantages of online services such as reduced average prices of online shops, time saving as one shops conveniently from home and the cost of locomotion and disrupts the potential gains of social distancing during a communicable pandemic leading to exposure to potential public health danger.
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