recommended product reflects their personal preferences expressed in terms of percentage match of products recommended by contentbased filtering systems however consumers with a low need for cognition preferred collaborative filtering because they were more persuaded by the percentage of other people who purchased the recommended item which also promoted their intentions to buy the item such differences were not found for recommendations of search products information about which can be obtained by searching online both personality types preferred collaborative recommendation systems you can think of it as a sort of cognitive outsourcing said sundar a customer might see the ad for a smartwatch for example and see the features but think im not going to do the hard work of examining all the details and coming to a conclusion of which is better ill just outsource this to others if they say its a good smartwatch then theyll buy it according to liao most research into recommendation systems focuses on optimizing the suggestions of these systems these findings suggest that developers may need to consider other factors such as personality types and product types for improving the user experience of their systems rather than focusing solely on the accuracy of their algorithms suggestions a lot may depend on how users receive the information on the recommendations provided by the systems said liao it matters why these systems are providing the recommendations for products and experiences the researchers recruited 469 people on an online crowdsourced microtask site for the study and randomly assigned them to an experimental website that either used a collaborative or content filtering algorithm for collaborative systems the researchers used a percentage range to indicate how many similar people used the recommended product or percentage match and serve as a cue for the bandwagon effect for contentbased systems the same percentage numbers were used to suggest the extent to which the recommended product matched the consumers personal characteristics based on their user profile there were three levels of percentage match indicators low medium and high in testing the two different types of products search and experience the researchers used a smartwatch recommendation as an example of a search product and a tourism destination recommendation to explore participants reactions to experience products before they browsed the ecommerce site all participants responded to a series of questions to determine whether they were a high need for cognition or a low need for cognition personality types because the researchers only tested two products and two common recommendation systems future research could look at the psychological effects of other systems and investigate other types of products the researchers said this could help verify the validity of their findings also read well ensure minimal consumer impact hul chairman sanjiv mehta ani published on may 5 2021 at 1219 pm ist telegram facebook copy link be the first one to comment comment now comments comment now read comment 1 all comments post post find this comment offensive choose