IJBTT

Improving Online Experience Using Trust Adjustment Factor for Recommender Systems
	© 2021 by IJCTT Journal
	Volume-69 Issue-4
	Year of Publication : 2021
	Authors : Edwin Ouma Ngwawe, Elisha Odira Abade, Stephen Nganga Mburu
	DOI :  10.14445/22312803/IJCTT-V69I4P114

How to Cite?

Edwin Ouma Ngwawe, Elisha Odira Abade, Stephen Nganga Mburu, "Improving Online Experience Using Trust Adjustment Factor for Recommender Systems," International Journal of Computer Trends and Technology, vol. 69, no. 4, pp. 83-89, 2021. Crossref, https://doi.org/10.14445/22312803/IJCTT-V69I4P114

Abstract
With the improvement of computing Technologies, online shopping has become a norm to the way of life. Aggravated by the global pandemic, COVID 19, the need for social distancing has even increased the demand for online shopping as people try to stay or work at home but still run their day-to-day errands. Unscrupulous vendors have realized this sudden shift to the online market and are trying to take advantage of inexperienced shoppers by luring them using various techniques and finally ending up defrauding them or harming them physically. There exist mechanisms to help shoppers choose the right product or services, such as the use of recommender systems to help users choose a product, but the loophole again is that the recommender systems depend on historic data to make their decisions. This data can still be manipulated by unscrupulous practitioners. There is a need to take care of this loophole in the recommender system. In this study, we carried out research on what Kenyans perceive as trust; we then used the Structural Equation Modelling technique to create a parameter known as Trust adjustment factor, which we are adding to the recommender system algorithm to take care of trust issues in online shopping.

Keywords
Ethics, Online fraud, Recommender systems, Structural equation modeling, Trust in online service providers.

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