Jadi, jika kita berfikir bahawa semua perkara yang saya percaya, ini sebenarnya adalah algoritm yang sangat generis yang boleh digunakan ke produk yang boleh digunakan, jika kita mempunyai langkah internasional. Jadi, ia tidak terlalu spesifik untuk mungkin semua produk. Jadi, ada yang boleh digunakan yang boleh digunakan. Dan perkara yang terlalu filam ini adalah bahawa比如 bagaimana algoritm terhadap dua perangkatan yang ditutupkan, mereka boleh mengalami potongan secara secara berbasis yang lain. Model semera-sebirdatah membantu dapat menganggalkan perangkat komplementar, perangkat komplementar. Kemudian dengan semula akaas.. Dan ia tidak akan lelaki yang lebih baik untuk mendukung televisi item kawasan nanossy, ini akan menjadi model yang lebih pudar tidak baik duga-duga untuk membuat wajah lebih jahat dan masyarakat membuat senjata yang lebih gampang untuk mengganti dan mendukung Wayangkan nanossy di kompetisi. So itu yang saya dah berdekapan, Saya bercakap tentang algoritma yang dapat membuat perempuan peribadi. Kami sebenarnya adalah perempuan perempuan perempuan. Ia mempunyai perempuan data yang sangat basic. Namun, ia masih perempuan perempuan yang penting untuk membuat perempuan perempuan. Yang penting adalah membuat perempuan perempuan. Sebelum saya berjaya untuk beritahu yang kami mempunyai, kita boleh menggantikan perempuan yang kami mempunyai. Ini hanya untuk perempuan data yang kami mempunyai. Jadi apabila mempunyai perempuan perempuan, perempuan perempuan, perempuan perempuan mereka akan mengakutkan perempuan. Perempuan perempuan perempuan perempuan akan mempunyai perempuan. Jadi, ia akan baik untuk mereka sesuatu. So, you want to do more comparison, do more evaluation for recommendations, right? Most of them, you will have to do it as a detailer. So, we forgot to do personal recommendations. A way you can do comparison is when you are showing the component on the homepage, you compare the click through rates in real time. Sorry, online, against other components within that same page. And the other components must function the same way in the sense that they must show products that actually, when you use a click, it leads to the bloody news page. So, once we have that kind of comparison that can be done, we can run it with test and see whether showing the personal recommendations lead to higher click-through rates from the homepage to the item detail page. Whether it leads to better click-through rate than not having it there. That's the answer question. So, comparing click-through rate. Ya. So, for the homepage, we have an aggregator click-through rate from the homepage to the item detail page without the personal recommendations component as well as with the personal recommendations component that lead to every point that we get a higher click-through rate if the component is shown to the user. Hai, I'm Shuman Cho. I'm passing my master's on a list. I have one question. A couple of questions. I have a few slides that you mentioned for a different group. I believe for portal sites like Amazon, we are in a few restaurants item which is usually popular item in those gamas. So, unpopular group. So, ya. How is practical principle applicable in particular to portal like Traveloca? So, my second question. There are three models that you will name in the initial slide of every model. You mentioned that you removed the scene items. Ya. Was it to counter the battle? Okay, so for your first question is how do I make sure that it's not recommended just popular items? Is that what I say? Is that your question? Essentially, it's like all the popular items coming up and being sold in majority. Ya. So, that's more applicable to general e-commerce. Ya. How is it in particular applicable to portal like Traveloca itself like it gets numerous current or hotels? So, how do we prevent each of them that we are just recommending popular items? Is that what I say? Ya. So, it's usually very hard to avoid that because if we are doing models based on user interaction, it's more likely that popular items will show up. So, the benefit of doing so the benefit of doing cloud-based embedded is that actually, this bias term here that will be very high for popular items. So, as a result popularity is a counter fall within these two bias values. And then the bias that we write here is W, will have values that account for the popularity of the item. So, you can use models to remove popular items to account for the popularity of the item. The other way, very cheerful is that when you have finally committed items, you also maintain a list of popular items that you filter away the popular items here. So, your second question is to last in item, right? Ya. The same thing, we don't want to recommend nothing other than that the user is really aware of the item, right? So, we should not waste our time to show them things that they are really aware as happening. They are really aware and available and I infer that by then I can really click on the product news page and they really know these items. So, we will be from the personnel recommendations. So, my question is did you build one big recommendation for your entire item or did you build a recommendation for each product category such as flight or hotel? Generally, I will train separate recommendations and tune for different product time. One is that so, especially if your product time is very diverse within the company the amount of interaction that's within different product groups will have different frequency. So, if we put them together in one one transition data it may not perform the way we think it will because the transition the way we aggregate the combination is different frequency implies different parts of signal. So, if we combine products which have different usage because of the major product it may not lead to better recommendations and better recommendations for others. And as a result of this it also means that for each recommendation component they currently recommend one kind of product. This is useful for different recommendations based on which the user is on. Of course, if it's on the homepage we probably want to do a cross product recommendations if we want to do that we need to really think about how we put the data together before we do any form of application. I have another question. Is there any system to recommend a kind of combination of product? If you're a hotel in KL then you can also recommend some transportation or any other services around KL is there any system to do that? There's nothing off the shelf of course you need to understand your customer journey this requires a lot of looking through data having specialized teams to understand how the user use your product to infer what kind of files they're going to use if I'm going to buy a flight ticket to KL if I want to buy a hotel in KL if I want to look at attractions in KL if they're buying a business class ticket then probably they're not just saying this is one hotel. So how do you take account all this inside of a commercial engine there's not something generic that can work, you need to understand how the customer journey varies depending on the usage just a quick question what percentage of your users actually sign in or you can go and track them so the trick to this is that I'm not actually using sign-in users but rather I'm using cookies so cookie ID is unique based on the app install based on the browser so we do not actually require users to be locked in in order for us to use their data having locked in users is useful if we want to make inferences or cross-evaluate usage but in general this is not a problem because users tend to seek to using website Indonesia i.e. Asia is most of our first country so in fact we find that the stock usage is quite low so if you're using a mobile phone unless you install an app the cookie ID is going to be almost similar to the profile ID my name is Ah Wei so this question is all on the app view can you share what's the business impact that your work has brought about go for the language modeling portion fantastic business impact for recognition i.e. 3-4 ways to if we can recommend various items it will definitely increase the usage the engagement of the app by our users of course if you can measure user clicks based on recommendation then it will be obvious how we can justify our value based on the revenue generated from recommendations this is i don't have a number unfortunately recommendations is still a very new idea between triangle guidance doing machine learning recommendations so it's still in the process of getting this and measuring so i'll tell you a question on the second part i think it comes out of two things the way we approach the problem is before we work on something we try and make sure it's aligned we'll actually solve in some of these spots and then the owner says we'll come to us and say we've got a couple of different ideas and then we have a few so this is what you want as a product what do you think is the impact to your business to your business and so on and that forces us both to be honest both from a product perspective so that a we ensure with a limited resource we have a working one in terms of actual dollars or i don't know if it makes sense to talk about that but i will say so for example some of the work that we've done we were looking at so for all teams we're looking at tens of thousands of reviews today trying to make sure that we're providing the same experience for our users and that sort of workload has come down significantly so we're helping them skid out our businesses so that's basically one last question i'm sure the speakers will stay around after this okay so my question first you mentioned about engineering working most difficult part about building the model and i wonder what the difficulty and also during the engineering part your business team do you seek input from them so i can repeat with your business team do you seek information or insights from your business team where you don't know what the data or how to make sense of the data at the initial stage so the first question is regards to the engineering demands of this model what is difficult is that usually when we are doing of the user interaction these are these are usually recorded in realtime but then they are inserted in the database in a batch list which means i'm only able to get a data that is 180 so if that's the case then my recommendations will be based on your interaction i'm not able to react to what you're clicking right now if i have that available it means that i need to increment tracking that inserts in realtime into something that's in memory so that i can i'm able to access it also in realtime at low latency for me to get all this information because even if it's inserted in realtime and retrieve then the recommendation will go very slowly it will deteriorate the user experience and they may not even be able to see your recommendations if that's the case so that's the main reason why there's physical engineering requirements for this sorry the second question how do you work for this model if i were to increment this model i do not have to work with the business that i'm working on doing recommendation products of course in order to customize the recommendation engine and to ensure that it's highly performant and also get some insights on the customer journey we need to work with the business units so in capital car business units will have their own data in this that have the understanding of the usage of users on the type of business for example hotels or attractions we have business artists that have good understanding of the form of data the patterns that we can look out for and then okay can we give a round of applause for speakers okay okay


esteemed colleagues, dear participants in the Global Conference on Addiction and Behavioral Health in London, the United Kingdom, August 2019. My name is Sandak Min, and this is my video presentation. I'm a professor of psychology in Southern Federal University, or Stovon Don, in the Russian Federation, and I'm also a professor of psychology and a professor of finance in CS, CIAPS, the Center for International Advanced and Professional Studies. Today I would like to discuss an unusual pathology, an unusual addiction, which conventionally is not perceived as such, and that would be love. The unpalatable truth is that falling in love, in some ways, is indistinguishable from a severe pathology. Behavioral changes are reminiscent of psychosis. Biochemically speaking, passionate love closely imitates substance abuse. As early as 2002, appearing in the BBC series Portuguese, Dr. John Marsden, at that time, the head of the British National Addiction Center, said that love is addictive, akin to cocaine or speed. Sex is a kind of booby trap intended to bind the partners long enough in order to bond. When we use functional magnetic resonance imaging, fMRI, we see interesting things. Andrea's battles and Samir Zicky of University College in London showed that the same areas of the brain are active when abusing drugs and when we're in love. The two situations are virtually indistinguishable. The prefrontal cortex, hyperactive in depressed patients, is inactive when besotted. How can this be reconciled with the low levels of serotonin that are the telltale signs of both depression and infatuation? We don't have the answer. There are still many mysteries. Other MRI studies conducted in 2006 and 2007 by Dr. Lucy Brown, a professor in the Department of Neurology and Neuroscience at the Albert Einstein College of Medicine in New York and some of her colleagues, these studies revealed that the cow date and the ventral tegmental, the areas of the brain involved in cravings, for example, for food, and the secretion of dopamine, these areas are lit up in subjects who view photos of their loved ones. Dopamine is a neurotransmitter that affects pleasure and motivation. It causes a sensation akin to a substance-induced high. On August 14, 2007, the New Scientist News Service gave the details of a study originally published in the Journal of Adolescent Health earlier that year. Serge Brandt of the Psychiatric University Clinic in Basel, Switzerland and his colleagues interviewed 113 teenagers. There were about 17 years old. 65 of these 113 reported having fallen in love recently, and the conclusion? The love-struck adolescents slept less, acted more compulsively and more often, and had lots of ideas and creative energy, as they describe. They were more likely to engage in risky behavior, such as reckless driving. The study concludes, we were able to demonstrate that adolescents in early age-intense romantic love did not differ from patients during a hypomanic stage. This led the researchers to conclude that intense romantic love in teenagers is a psychopathologically prominent stage. But is it erotic lust, or is it love that brings about these cerebral upheavals? As distinct from love, lust is brought on by surges of sex hormones, such as testosterone and estrogen. These hormones induce an indiscriminate scramble for physical gratification. In the brain, the hypothalamus, which controls hunger, thirst, and other primordial drives, and the amygdala, the locus of arousal, they become active. Attraction transpires once a more or less appropriate object is found, with the right body language and speed and tone of voice. And it results in a panoply of sleep and eating disorders. A recent study at the University of Chicago demonstrated that testosterone levels shoot up by one third even during a casual chat with a female stranger. The stronger the hormonal reaction, the more marked the changes in behavior concluded the authors. And this loop may be part of a larger mating response. In animals, testosterone provokes aggression and recklessness. The hormones readings in married men and fathers are markedly lower than in single males still playing the field. And still, the long-term outcomes of being in love are lustful. Dopamine, heavily secreted while falling in love, triggers the production of testosterone and sexual attraction then kicks in. Helen Fisher of Roger University suggests a three-phase model of falling in love. Each stage involves a distinct set of chemicals. The BBC summed it up succinctly and sensationally, events occurring in the brain where wearing love have similarities with mental illness. This author does not beg to tiff her. Moreover, we are attracted to people with the same genetic makeup, with the same smell, pheromones of our parents. Dr. Martha McClintock of the University of Chicago studied feminine attraction to sweaty t-shirts formerly worn by males. The closer the smell resembled her father's, the more attracted and aroused the woman became. Falling in love is therefore an exercise in proxy insist, and a vindication of Freud's much-maligned early-poose and electro complexes. Writing in the February 2004 issue of the journal Neuroimage, Andreas Bartels of University College London's Welcome Department of Imaging Neuroscience described identical reactions in the brains of young mothers looking at their babies and in the brains of people looking at their lovers. Both romantic and maternal love are highly rewarding experiences that are linked to the perpetuation of the species and consequently have a closely linked biological function of crucial evolutionary importance, at least that's what he told Reuters. This incestuous backdrop of love was further demonstrated by psychologist David Parrott of the University of St. Andrews in Scotland. The subjects in his experiments preferred their own faces, in other words the composite of their two parents, when computer morphed into the opposite sex. And so we go on in our tour of love land. Body secretions play a major role in the onslaught of love. In results published in February 2007 in the Journal of Neuroscience, researchers at the University of California at Berkeley demonstrated convincingly that women who sniffed under prostadionone, a signaling chemical found in male sweat. So women who smelled this chemical, which is also found in saliva and semen, experienced higher levels of the hormone cortisol. This results in sexual arousal and improved mood. The effect lasted a whopping one hour. So if a woman smelled the sweat, or somehow the saliva let along the semen, or the man and it inevitably contained understadionone, she felt much better, her mood improved, and she got sexually aroused, and this lasted a whole hour. And still, contrary to prevailing misconceptions, love is mostly about negative emotions, not about positive ones. As Professor Arthur R. Aaron from the State University of New York at Stonybrook had shown, in the first few meetings, people misinterpret certain physical cues and feelings, notably fear or thrill, as falling in love. They feel fear and say I'm falling in love. They get excited and they say well I must be falling in love. Just counter-intuitively, anxious people, especially those with the serotonin transporter gene, anxious people are more sexually active and falling in love much more often, because they misinterpret their anxiety, the misinterpreted cues. Obsessive thoughts regarding the loved one and compulsive acts are also very common. Deception is distorted, as is co-opinion. Love is blind and the lover easily fails the reality test. Falling in love involves the enhanced secretion of phenylatilamine, PEA, or the love chemical, and this happens in the first two to four years of the relationship. And this natural drug, PEA, creates a euphoric high and helps obscure the failings and shortcomings of the potential mate. Such oblivion, perceiving only the spouse's good sides while discarding her bad ones, is a pathology akin to the primitive psychological defense mechanism known as splitting or dichotomous thinking. Narcissists, patients suffering from narcissistic personality disorder, also idealize romantic or intimate partners. The similar cognitive-emotional impairment is common in many other mental health conditions, and in love. The activity of a host of neurotransmitters, such as dopamine, adrenaline, or norepinephrine, and serotonin, the activity of these neurotransmitters is heightened, or in the case of serotonin lowered, in both paramours. Such irregularities are also associated with obsessive-compulsive disorder, for example, or with depression. It is telling that once attachment is formed, and infatuation gives way to a more stable and less exuberant relationship, the levels of these substances return to normal. They are replaced by two other hormones, which belong to the family of endorphins. These two other hormones usually play a part in social interactions, including bonds and sex. The first one is oxytocin, the cuddling chemical, and vasopressin. Oxytocin's facility is bonding, it is released in the mother during breastfeeding, in the members of a couple, when they spend time together, and also when one sexually climaxes. Viagra, sildenofil, seems to facilitate its release, at least in rats. It seems, therefore, that the distinctions we often make between types of love, motherly love versus romantic love, for instance, these distinctions are artificial, as far as human biochemistry goes. As neuroscientist Larry Young's research with Ferry Volts in the Yerks National Primate Research Center at Emory University, as this research demonstrates, I quote, human love is set off by a very chemical chain of events that originally evolved in ancient brain circuits involving child-mother bonding, and which is stimulated in mammals by the release of oxytocin during labor, delivery, and nursing. The same Larry Young told the New York Times, in January 2009, in the article titled, Anti-Love Drug May Be Ticket to Bliss, he told the New York Times, some of our sexuality has evolved to stimulate that same oxytocin system to create female-male bonds. Dr. Young said, noting that sexual foreplay and intercourse stimulate the same parts of the woman's body that are involved in giving birth, and nursing. This hormonal hypothesis, which is by no means proven fact, would help explain a couple of differences between humans and less monogamous mammals. Females desire to have sex even when they are not fertile, and male's erotic fascination with breasts are all accounted for by this oxytocin system. More frequent sex and more attention to breasts, Dr. Young said, could help build long-term bonds through a cocktail of ancient neuropeptides, like the oxytocin released during foreplay or orgasm. Researchers have achieved similar results simply by squirting oxytocin into people's nostrils. Dr. Young continues, a related hormone, vasopressin, creates urges for bonding and nesting when it is injected in male voles, or naturally, activated by sex. After Dr. Young found that male voles with a genetically limited vasopressin response were less likely to find mates, Swedish researchers reported that men with a similar genetic tendency were less likely to get married. If we even oxytocin blocker to female voles, they become like 95% of all mammal species, Dr. Young said. They will not bond, no matter how many times they mate with a male or try to bond. They mate, it feels really good and they move on, if another male comes along. If love is similarly biochemically based, you should in theory be able to suppress it in a similar way. Love in all its phases and manifestations is an addiction, probably to the various forms of internally secreted neuropinephrine, and norepinephrine, such as the aforementioned a fetamine like PEA. In other words, love is a form of substance abuse. The withdrawal of romantic love has serious mental health repercussions. A study conducted by Dr. Kenneth Kendler, professor of psychiatry and director of the Virginia Institute for Psychiatric and Behavioral Genetics, and by others, this study, published in September 2002, issue of Archives of General Psychiatry, revealed that breakups often lead to depression and anxiety. Other FMRI-based studies demonstrated how the insular cortex, in charge of experiencing pain, became active when subjects viewed photos of former loved ones. Love and lust depend on context, as well as psychological makeup or biochemistry. One can fall in and out of love with the very same person, whose biochemistry presumably hasn't changed at all. The vast majority of one-night standards reported that they did not find their partners sexually alluring. It was the opportunity that beckoned not any specific attraction. And similarly, the very same acts, kissing, hugging, even sexually explicit or maturation, can be interpreted as innocuous, depending on who does what to whom, and in which circumstances. Indeed, love cannot be reduced to its biochemical and electrical components. Love is not tantamount to our bodily processes. Love is the way we experience these bodily processes. Love is how we interpret these flaws and ebbs of compounds using a higher level language. In other words, love is pure poetry, pure language. We are very rarely in love with a person. Most often we are in love with an idea, the idea of being in love. We are in love with love itself. We are in love with the idea of being someone's whore or someone's child or someone's savior or someone's healing parent, or we're in love with what the person stands for, symbolizes father figure or a past, a wounded child. We idealize our loved ones to the point that they vanish as individuals and reemerge as elements in our personal narrative and in our pathologies and in our wounds. We fall in love with the stories that we construct about ourselves and about our environment. We force our loved ones to play scripted and emergent roles in our personal theater production. This is called projective identification. In this restricted and temporary sense, when we fall in love, we are all narcissistic. We fall in love with ourselves via our loved ones, hence the addiction.