We propose an end-to-end real-estate recommendation system, RE-RecSys, which has been productionized in real-world industry setting. We categorize any user into 4 categories based on available historical data: i) cold-start users; ii) short-term users; iii) long-term users; and iv) short-long term users. For cold-start users, we propose a novel rule-based engine that is based on the popularity of locality and user preferences. For short-term users, we propose to use content-filtering model which recommends properties based on recent interactions of users. For long-term and short-long term users, we propose a novel combination of content and collaborative filtering based approach which can be easily productionized in the real-world scenario. Moreover, based on the conversion rate, we have designed a novel weighing scheme for different impressions done by users on the platform for the training of content and collaborative models. Finally, we show the efficiency of the proposed pipeline, RE-RecSys, on a real-world property and clickstream dataset collected from leading real-estate platform in India. We show that the proposed pipeline is deployable in real-world scenario with an average latency of <40 ms serving 1000 rpm.

Male and female lar gibbons sing duets with notes that are synchronised and occur at regular intervals. These are rhythmic qualities similar to those found in human songs, which could hint at an evolutionary basis for the origins of music. "I'm pretty sure the gibbon's isochronous capacities are better than mine," says Andrea Ravignani at the Max Planck Institute for Psycholinguistics in the Netherlands, referring to the capacity to sing notes that occur at regularly repeating intervals. This ability has previously been noted in indris (Indri indri), a type of lemur found in Madagascar and the only other primate whose calls exhibit distinct rhythms related to those found in human music. Male and female gibbons regularly sing duets to define territory and form social bonds.

According to a top consulting report, if the Industry gets it right, linking the physical and digital worlds could generate up to $11.1 trillion a year in economic value by 2025. These have resulted in the exponential growth of the data generated through the IoT devices, which has created a requirement to bring computational power at individual device levels using edge computing rather than sending data to the cloud for analysis. Edge computing can move parts of the service-specific processing and data storage from the central cloud/datacenter to edge network nodes; when combined with Artificial Intelligence (AI), it can bring intelligence at the device level. This help to build a smart/intelligent connected network of edge devices called Edge AI or Edge AIoT (Artificial Intelligence of Things) or Intelligent Internet of Things. To know more about Edge AI please check out our blog on Edge AI: The Era of Distributed AI Computing.

I have not written the hereunder and these are the notes from the book. This blog does not contain any "spoilers" because its a non-fiction. You can absolutely read the blog and then decided it you want to read the book or not. If you read the book, then you can refer back here if you forget something. I have jotted this down because these are really interesting facts that I like to revise.

Machine learning (ML) is widely regarded as the cornerstone of digital transformation, yet ML models are the most susceptible to the changing dynamics of a digital landscape. ML models are defined and optimized by the variables and parameters available at the time period in which they are created. Let us look at the case of an ML model created to track spam emails based on a generalized template of spam emails that may have been proliferating at the time. With this baseline in place, the ML model is able to identify and stop these sorts of emails, thus preventing potential phishing attacks. However, as the threat landscape changes and cybercriminals become smarter, more sophisticated and realistic emails have replaced the old ones.

It has been the rule of nature and society that when applying technologies like Artificial Intelligence in the workforce or daily lifestyle increases, it eventually reduces human resources or even eliminates their need. A perfect example could be mailing and logistical services. With technology advances in the last couple of decades, these services have been very smooth and efficient, resulting in the loss of many jobs and even reducing staffing resources for more profit generation. Today, tracing a shipment or raising any grievance related to that is just a matter of seconds. Is AI also following the same track when it comes to workforce resources?

Time Series is defined as a set of observations taken at a particular period of time. For example, having a set of login details at regular interval of time of each user can be categorized as a time series. On the other hand, when the data is collected at once or irregularly, it is not taken as a time series data. Stock Series - It is a measure of attributes at a particular point in time and taken as a stock takes. Flow Series - It is a measure of activity at a specific interval of time. It contains effects related to the calendar. Time series is a sequence that is taken successively at the equally pace of time. It appears naturally in many application areas such as economics, science, environment, medicine, etc. There are many practical real life problems where data might be correlated with each other and are observed sequentially at the equal period of time.

Time Series is defined as a set of observations taken at a particular period of time. For example, having a set of login details at regular interval of time of each user can be categorized as a time series. On the other hand, when the data is collected at once or irregularly, it is not taken as a time series data. Time series is a sequence that is taken successively at the equally pace of time. It appears naturally in many application areas such as economics, science, environment, medicine, etc.