Initial experimental evidence that the ability to choose between items alters attraction to familiar versus novel persons in different ways for men and women

Nonhuman species may respond to novel mates with increased sexual motivation (‘The Coolidge Effect1). In humans, novel technological advances, such as online dating platforms, are thought to result in ‘Choice Overload’2. This may undermine the goal of finding a meaningful relationship3, orienting the user toward novel possible partners versus committing to a partner. Here, we used a paradigm measuring change in attraction to familiar faces (i.e. rated on second viewing4) to investigate Coolidge-like phenomena in humans primed with choice of potential online dating partners. We examined two pre-registered hypotheses (https://osf.io/xs74r/files/). First, whether experimentally priming choice (viewing a slideshow of online dating images) directly reduces the attractiveness of familiar preferred sex faces compared to our control condition. Second, whether the predicted effect is stronger for men than women given the role of the Coolidge effect in male sexual motivation5.<br/

The great porn experiment V2.0:sexual arousal reduces the salience of familiar women when heterosexual men judge their attractiveness

Pornography has become widely accessible in recent years due to its integration with the Internet, generating social scientific and moralistic debate on potential “media effects,” given correlations between consumption and various sexual traits and behaviors. One popular public debate (Wilson, 2012) claimed that exposure to Internet pornography has addictive qualities that could impact men’s sexual relationships, underpinned by the “Coolidge effect,” where males are sexually motivated by the presence of novel mates. As claims about Internet and sexual addictions are scientifically controversial, we provide a direct experimental test of his proposal. Adapting a paradigm used to examine “Coolidge-like” effects in men, we examined the extent to which exposure to images of pornographic actresses altered men’s attractiveness ratings of (1) familiar faces/bodies on second viewing and (2) familiar versus novel women’s faces/bodies. Independent of slideshow content (pornographic versus clothed versions of same actress), heterosexual men were less attracted to familiar bodies, and homosexual men were less attracted to familiar women (faces and bodies), suggesting that mere visual exposure to attractive women moderated men’s preferences. However, consistent with one of our preregistered predictions, heterosexual but not homosexual men’s preferences for familiar versus novel women were moderated by slideshow content such that familiar women were less salient on the attractiveness dimension compared to novel women when sexual arousal was greater (pornographic versus clothed slideshows). In sum, our findings demonstrate that visual exposure/sexual arousal moderates attractiveness perceptions, albeit that much greater nuance is required considering earlier claims.</p

Fast Matrix Factorization for Online Recommendation with Implicit Feedback

This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our eALS method consistently outperforms state-of-the-art implicit MF methods. Our implementation is available at https://github.com/hexiangnan/sigir16-eals.Comment: 10 pages, 8 figure

Having options alters the attractiveness of familiar versus novel faces:sex differences and similarities

Although online dating allows us to access a wider pool of romantic partners, choice could induce an ‘assessment mindset’, orienting us toward ‘optimal’ or alternative partners and undermining our willingness to commit or remain committed to someone. Contextual changes in judgements of facial attractiveness can shed light on this issue. We directly test this proposal by activating a context where participants imagine choosing between items in picture slideshows (dates or equally attractive desserts), observing its effects on attraction to i) faces on second viewing and ii) novel versus familiar identities. Single women, relative to single men, were less attracted to the same face on second viewing (Experiments 2 and 4), with this sex difference only observed after imagining not ‘matching’ with any romantic dates in our slideshow (i.e., low choice, Experiment 4). No equivalent sex differences were observed in the absence of experimental choice slideshows (Experiment 3), and these effects (Experiment 2) were not moderated by slideshow content (romantic dates or desserts) or choice set size (five versus fifteen items). Following slideshows, novel faces were more attractive than familiar faces (Experiment 1), with this effect stronger in men than in women (Experiment 2), and stronger across both sexes after imagining ‘matching’ with desired romantic dates (i.e., high choice, Experiment 4). Our findings suggest that familiarity does not necessarily ‘breed liking’ when we have the autonomy to choose, revealing lower-order socio-cognitive mechanisms that could underpin online interactions, such as when browsing profiles and deciding how to allocate effort to different users