Social activities play an important role in people's daily life since they interact. For recommendations based on social activities, it is vital to have not only the activity information but also individuals' social relations. Thanks to the geo-social networks and widespread use of location-aware mobile devices, massive geo-social data is now readily available for exploitation by the recommendation system. In this paper, a novel group recommendation method, called attentive geo-social group recommendation, is proposed to recommend the target user with both activity locations and a group of users that may join the activities. We present an attention mechanism to model the influence of the target user $u_T$ in candidate user groups that satisfy the social constraints. It helps to retrieve the optimal user group and activity topic candidates, as well as explains the group decision-making process. Once the user group and topics are retrieved, a novel efficient spatial query algorithm SPA-DF is employed to determine the activity location under the constraints of the given user group and activity topic candidates. The proposed method is evaluated in real-world datasets and the experimental results show that the proposed model significantly outperforms baseline methods.

Link Building is a part of Search Engine Optimisation and is usually referred to as Off-Page. Unlike, On-Page – Link building is an ongoing process. For the sake of not getting into a debate, I will call "on-page" - a onetime process). Link Building starts as soon as you finish on-page work on the website. In layman's language - Link building is a method to get references from other websites to your website.

Bing announced a new link penalties. These link penalties are focused on taking down private blog networks (PBNs), subdomain leasing and manipulative cross-site linking. An inorganic site structure is a linking pattern that uses internal site-level link signals (with subdomains) or cross-site linking patterns (with external domains) in order to manipulate search engine rankings. While these spam techniques already existed, Bing introduced the concept of calling them "inorganic site structure" in order to describe them. Bing noted that sites legitimately create subdomains to keep different parts of the site separate, such as support.example.com.

Pinterest has updated the look of its app for iOS and Android with a renewed focus on personalized recommendations and a more efficient use of space. The most obvious change when opening the app after the update is the reduced space around pins. Pinterest is cramming the home feed with as much content as it can while still keeping things aesthetically pleasing. The next-most obvious change is the carousel of personal recommendations at the top of the home feed. You can scroll through and tap on one of the topics of interest to open up a dedicated feed.

Searching on Google has become second nature for billions of people. In fact, Googling is so entrenched in our culture, it's become the generic word for looking things up online. Yet even though you long ago mastered the essentials, there's still a lot most of us can learn about how to search faster and more effectively. Senior Google research scientist Daniel M. Russell recently published "The Joy of Search: A Google Insider's Guide Going Beyond The Basics." USA TODAY caught up with Russell in New York where he was teaching a "Grow With Google" class on search strategies.

Slyce was born from a simple vision: What if you could find and buy any product you see, using the technology of visual search on a photograph? Of course, the task of visual product search is born of a more ambitious challenge: Given a query image, how do you determine the most relevant images from a universe of images and presenting them in decreasing order of relevancy? In the case of a traditional search engine, for example, the universe would be all images that are available on the internet. For a social network, it would be all images that were posted or pinned on its website (or app). In our case we elected to focus on solving visual search for retailers, narrowing the universe to be the entire catalog of products that a given retailer sells.

Note: By submitting this form, you agree to Third Door Media's terms. Google is making the largest change to its search system since the company introduced RankBrain, almost five-years ago. The company said this will impact 1 in 10 queries in terms of changing the results that rank for those queries. BERT started rolling out this week and will be fully live shortly. It is rolling out for English language queries now and will expand to other languages in the future.

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Google has released new behavioural insights into 2019 holiday shoppers, with tips on how to sell to US consumers. According to Google's data, mobile searches for "best deals" have grown by 90%. So it should come as no surprise that the #1 factor when consumers decide where to buy is whichever retailer has the lowest price. Consumers also appreciate being able to do what they want all on their own. It's also interesting to note that searches around "rewards apps" and "Black Friday deals" are up 200% this year.

The explosion in unstructured data, such as images, videos, sound records, and text, requires an effective solution for computer vision, voice recognition, and natural language processing. How to extract value from unstructured data poses as a big challenge for many enterprises. AI, especially deep learning, has been proved as an effective solution. Vectorization of data features enables people to perform content-based search on unstructured data. For example, you can perform content-based image retrieval, including facial recognition and object detection, etc.