No doubt it’s been done before, but not by me
But clearly, I was a profound disappointment. But I enjoyed myself enourmously.
|cloth||woven or felted fabric made from wool, cotton, or a similar fiber.|
|palpable||able to be touched or felt.|
|gushing (v)||(of a liquid) flow out in a rapid and plentiful stream, often suddenly.|
|triumph||achieve a victory; be successful.|
|quench||satisfy (one’s thirst) by drinking.|
|inferior||lower in rank, status, or quality.|
|plew||a beaver skin, used as a standard unit of value in the fur trade.|
|hoop||a circular band of metal, wood, or similar material, especially one used for binding the staves of barrels or forming part of a framework.|
|ethnicity||the fact or state of belonging to a social group that has a common national or cultural tradition.|
|psyche||the human soul, mind, or spirit.|
|clam||a marine bivalve mollusk with shells of equal size.|
|oyster||any of a number of bivalve mollusks with rough irregular shells. Several kinds are eaten (especially raw) as a delicacy and may be farmed for food or pearls.|
|timber||wood prepared for use in building and carpentry.|
|denigration||calumny, slander, defamation, aspersion|
|imprint||a mark made by pressing something onto a softer substance so that its outline is reproduced.|
|betterment||the act or process of improving something.|
|“The moral imperative of our generation”|
|edifice||A building, especially a large one – عمارت|
|obscurity||The state of not being known or remembered – گمنامی، تیرگی|
|oasis||A peacful or pleasant place that is very different from everthing around – واحه، آبادی یا مرغزار میان کویر|
|intrigue||To make secret plans to harm someone or make them lose their position of power – فتنه، دسیسه|
|Quest||A long search for something that is difficult to find – بازجویی، جستجو|
|Expedition||A short journey usually made for a particular purpose – سفر، هیئت اعزامی|
|Topple||to take power away from a leader or goverment, especially by force, overthrow – واژگون کردن|
|Conquest||The act of getting control of a country by fighting – پیروزی، تسخیر، غلبه|
|Gruesome||very unpleasant or shocking, and involving someone being killed or badly injured – مخوف، نفرت انگیز، مهیب|
|Conquistadores||one of the Spanish conquerors of Mexico and Peru in the 18th century –|
|Deity||A god or godess – خدایان|
An exploration of three of the world’s most iconic rivers, the Nile, the Mississippi, and the Amazon, with a look at the wildlife, astonishing landscapes, and remarkable people who live along their banks.
Wind (v): move in or take a twisting or spiral course.
Swathe (n): a broad strip or area of something.
Tributary (n): a river or stream flowing into a larger river or lake.
Upstream (adv): in the opposite direction from that in which a stream or river flows; nearer to the source.
Headwaters (n): The streams that form a river.
Scale (v): To climb to the top of something that is high and difficult to climb.
Sheer (adj): A sheer drop, cliff, slope, etc is very steep and almost vertical.
Geyser (n): A natural spring that sends hot water and stream suddenly into the air from a hole in the ground.
Spew (v): To flow out something quickly in large quantities, or to make something flow out this way.
Icebound (adj): completely surrounded or covered by ice.
Meander (n): a winding curve or bend of a river or road.
Detour (n): a long or roundabout route that is taken to avoid something or to visit somewhere along the way.
Labyrinth (n): a complicated irregular network of passages or paths in which it is difficult to find one’s way; a maze.
Cypress (n): an evergreen coniferous tree with small rounded woody cones and flattened shoots bearing small scale-like leaves. درخت سرو
Drape (v): arrange (cloth or clothing) loosely or casually on or around something.
Moss (n): a small flowerless green plant that lacks true roots, growing in low carpets or rounded cushions in damp habitats and reproducing by means of spores released from stalked capsules. خزه
Waterlogged (n): saturated with or full of water.
Endearing (adj): inspiring love or affection.
Hatch (v): (of a young bird, fish, or reptile) emerge from its egg.
Egret (n): a heron with mainly white plumage, having long plumes in the breeding season. مرغ ماهی خوار سفید
Bask (v): lie exposed to warmth and light, typically from the sun, for relaxation and pleasure.
Levee (n): an embankment built to prevent the overflow of a river.
Hem (v): surround and restrict the space or movement of.
Lagoon (n): a stretch of saltwater separated from the sea by a low sandbank or coral reef.
Wetland (n): land consisting of marshes or swamps; saturated land.
Barge (n): a flat-bottomed boat for carrying freight, typically on canals and rivers, either under its own power or towed by another.
Marshy (adj): characteristic of or resembling a marsh; waterlogged.
Brink (n): an extreme edge of land before a steep or vertical slope.
Crest (n): a comb or tuft of feathers, fur, or skin on the head of a bird or other animal.
Dashing (v): run or travel somewhere in a great hurry.
Sediment (n): matter that settles to the bottom of a liquid; dregs.
Erosion (n): the process of eroding or being eroded by wind, water, or other natural agents.
Dredge (v): clean out the bed of (a harbor, river, or other areas of water) by scooping out mud, weeds, and rubbish with a dredge.
User modeling assists us to predict users’ behavior and interaction. Originated user model from a user can be used in a personalized system in which the user is interacting with it, for example, applying to improve recommender systems. “Cold Start” is one of the principal challenges in user modeling, personalization and recommender systems which exists in all inner-system user modeling. This phenomenon causes sparse data on initial user data, which leads to an inaccurate forecast of the user’s behavior and then incorrect personalization and unsuitable recommendations. To overtake this problem, it is possible to use users’ public profiles on other social media accounts of his or hers. This approach is the definition of cross-system modeling. The problem we are trying to solve is retrieving metadata from the user’s public profile, which are presented on YouTube and Twitter in order to cause improvement in recommender systems personalization.
- Which features are more important in cross-system modeling on Twitter and YouTube?
- How accurately can we predict selected features using other features?
- What are the possible user models that we can plug into the features’ relationship?
We couldn’t use open-source data because of the ethical standpoint of available open-source data. All of them preserving users’ privacy, and they don’t provide real person names, addresses, or any other personal information, so we can’t look up for their other social accounts. We tackled this problem by using the most popular channels on YouTube. Using a questionnaire was not an option for our research as long as finding people with an active YouTube channel, and the Twitter account was not common for accessible participants. We have started our dataset with other dataset called “Top 5000 YouTube channels.”
Our steps to reach the ideal dataset were first adding Channel ID to the dataset. These ids helped us to crawl and fetch more data about each channel. Those data are but not limited to about page, channel’s latest videos and updates, and analytical data of YouTube on each channel. We pick out YouTube channels that had Twitter account links on their about page. Purged broken or protected accounts on Twitter and collected as much data on Twitter using Twitter API as much as we could. Example of these is Tweets, metadata on tweets, followers, and followings and metadata on them as well.
long the way of collecting such a dataset, we faced some unique challenges. Both Twitter and YouTube (Google) force a heavy restriction on their API usage for reasonable causes. That led to a more time-consuming task of testing and implementing cycle than usual. And as long as these target people in the final dataset are “internet fame,” testing on ordinary people is something that can be worked on future researches. At this point, we produced a dataset that intersected on both parties Twitter and YouTube, which is roughly 300 records.
For this research, we have only picked a few features of the entire dataset features, those that we assumed are the most important ones at the end:
- YouTube view count: Each channel on YouTube shows its total view count on the About page of that channel.
- YouTube subscriber count: Each channel on YouTube shows a total number of subscribers, people who will be notified of new content on that channel.
- YouTube uploaded video count: Total number of videos uploaded to a channel.
- Twitter follower count: Total number of people who follow a person on Twitter.
We utilized correlation heatmap to spot potential associations among picked features in our dataset. Following conclusions can be made base upon the heatmap:
- The close connection between subscriber count and total view count
- Near no-connection among uploaded video count and total view count
- Corresponding importance of Twitter follower count and YouTube subscriber count
Then we applied the regression algorithm using the Sklearn library in Python to predict on total view count of the YouTube feature. We could conclude the following results on our dataset:
- Average view count of 3,550,524,704.7
- Maximum Residual Error of 104.61
- The average absolute error of 27.27
- The average execution time of 372ms
It is feasible to use concluded data in the static and stereotype user model as an addition to available user model features and use it for recommendation and personalization. The regression algorithm is resulting in acceptable time and accuracy in comparison to the average of view count and size of the dataset. We can use Twitter follower feature instead of a YouTube subscriber count in case of mitigating the cold-start problem for a newly joined creator who is well-established on Twitter to make their content more discoverable.
- Check the content of images, videos, and texts of Tweets and videos on YouTube.
- Check YouTube links in a Tweet.
- Check based on YouTube channel classification.
- Check videos and tweets head to head.
- Add other common social media like Facebook and Instagram.
- Creating a system for collecting users’ public data for application on other social media.
This is a brief report of my master thesis titled “Cross-system social web user modeling personalization of recommender system” mainly focused on social computing between Twitter and YouTube to help YouTube creators, written originally in Persian at Shahid Beheshti University under supervision of Dr. Elaheh Homayounvala. The paper is underwriting.
So now at last we’ve come to this great problem, this question. The problem of mutual understanding. how can blind and sighted people truly understand each other? how can men understand women? how can the rich understand the poor? how can the old understand the young? can an we have insight into other people? this is the great question upon which the unity of our humanity hangs.
Aridity: (n) a deficiency of moisture (especially when resulting from a permanent absence of rainfall), (n) the quality of yielding nothing of value
Deprive: (v) deny (a person or place) the possession or use of something.
remorseless: without regret or guilt.
acquiescence: the reluctant acceptance of something without protest.
futile: incapable of producing any useful result; pointless.
contour: an outline, especially one representing or bounding the shape or form of something.
put up with: tolerate; endure.
scandalize: shock or horrify (someone) by a real or imagined violation of propriety or morality.
throbbing: causing pain in a series of regular beats.
swoop: (especially of a bird) move rapidly downwards through the air.
Cloak: an outdoor overgarment, typically sleeveless, that hangs loosely from the shoulders.
Abstract: User modeling assists us to predict users’ behavior and interaction. Originated user model from a user can be used in a personalized system which user is interacting with it, for example, using to improve recommender systems. “Cold Start” is one of the principal challenges in user modeling, personalization and recommender systems which exists in all inner-system user modeling. This phenomenon causes sparse data on initial user data, which leads to an inaccurate forecast of the user’s behavior and then incorrect personalization and unsuitable recommendations. To overtake this problem, it is possible to use users’ public profile on other social media accounts of his or hers. This approach is the definition of cross-system modeling. The problem we are trying to solve in this study is retrieving metadata from the user’s public profile, which are presented on YouTube and Twitter in order to cause improvement in recommender systems personalization. That being said, Application Programming Interface or API has been employed to mine the data, and 5000 YouTube social media records have been recorded. Structure of the mined data has been reviewed and analyzed to discard outdated and outlier data. In order to review the connection between user’s features in two systems, Regression algorithm has been examined for precision and runtime execution measures. Results showed that subscribers count of a channel has little to none relation to count of uploaded videos of that channel. Also, connection and the same advantage of Twitter followers count feature of a person to predict YouTube total view count on the user’s channel has been concluded. The outcome of this study can be applied in the improvement of the personalized recommender system in YouTube channels where they have begun freshly. In these circumstances, it is feasible to use the Twitter follower count feature alternatively to the YouTube subscriber count feature to moderate the cold start problem for that channel.
Keywords: Recommender systems, user modeling, social media, cross-system user modeling, personalization
ngrok http -host-header=rewrite site.dev:80
To the outside the dead leaves, they’re on the lawn
Before they died, had trees to hang their hope