small fixes (#83)

Author: cw00dw0rd

notebooks/arangoflix/similarMovie_MF_ML_Inference.ipynb

@@ -98,7 +98,8 @@
     "!pip3 install python-arango\n",
     "!pip install surprise\n",
     "!pip install faiss-cpu \n",
-    "!pip install datapane"
+    "!pip install datapane\n",
+    "!pip install pyyaml==5.4.1"
    ]
   },
   {

notebooks/arangoflix/similarMovie_TFIDF_AQL_Inference.ipynb

@@ -25,7 +25,9 @@
    },
    "source": [
     "# Content-based Recommendation Using ArangoSearch TFIDF and AQL\n",
-    "This is an implementation of content-based recommendation using ArangoSearch and AQL.  ArangoSearch TFIDF is used to compute similarity between movies.  An ArangoSearch view of Movie (MovieView) is created  containing  movie title, tagline, and overview  from the Movie collection in the Movie Knowledge Graph.  The simularity between user highly  rated movies and other movies is computed using ArangoSearch. An AQL query then finds the most highly rated movies by the specific user  and then uses the movie similarity to compute the set of movies most like the highest rated movies for the specific user"
+    "This is an implementation of content-based recommendation using ArangoSearch and AQL.  ArangoSearch TFIDF is used to compute similarity between movies.  An ArangoSearch view of Movie (MovieView) is created  containing  movie title, tagline, and overview  from the Movie collection in the Movie Knowledge Graph.  The simularity between user highly  rated movies and other movies is computed using ArangoSearch. An AQL query then finds the most highly rated movies by the specific user  and then uses the movie similarity to compute the set of movies most like the highest rated movies for the specific user\n",
+    "\n",
+    "[Understanding TF-ID: A Simple Introduction - MonkeyLearn Blog](https://monkeylearn.com/blog/what-is-tf-idf/)"
    ]
   },
   {
@@ -34,7 +36,7 @@
     "id": "lUHaQbYk9xEr"
    },
    "source": [
-    "#Setup Interactive Demo Environment"
+    "# Setup Interactive Demo Environment"
    ]
   },
   {
@@ -85,7 +87,7 @@
     "id": "pE28K_g8ridl"
    },
    "source": [
-    "#Configure Connection to ArangoDB Database\n",
+    "# Configure Connection to ArangoDB Database\n",
     "You will be setting an ArangoDB temporary database and restoring the movie recommendation demo to it.  You can also set up an empty ArangoDB database instance in your own environement (Local, Cloud, or Oasis) and  restore the recommendation demo to it.  Or you can configure the connection to an existing arangodb instance containing the movie recommendation demo."
    ]
   },
@@ -95,7 +97,7 @@
     "id": "qcyMLeOV-Cui"
    },
    "source": [
-    "##Setup Temporary Database for Interactive Demo Environment"
+    "## Setup Temporary Database for Interactive Demo Environment"
    ]
   },
   {
@@ -147,7 +149,7 @@
     "id": "OaC4QhCUhAAe"
    },
    "source": [
-    "#Restore the Movie Knowledge Graph"
+    "# Restore the Movie Knowledge Graph"
    ]
   },
   {
@@ -169,7 +171,7 @@
     "id": "-zoAB7ht-UQg"
    },
    "source": [
-    "#Run ArangoSearch TFIDF Content-based Recommendation Query"
+    "# Run ArangoSearch TFIDF Content-based Recommendation Query"
    ]
   },
   {
@@ -210,7 +212,7 @@
     "        RETURN {movie : DOCUMENT(recommendedMovieId) , score : aggregateScore}\n",
     "\"\"\"\n",
     "\n",
-    "bind = {'userId':'User/1', 'similarUserLimit': 10, 'movieRecommendationLimit':3}\n",
+    "bind = {'userId':'User/1', 'movieRecommendationLimit':3, 'topRatedMovieLimit': 10}\n",
     "movieData = db.AQLQuery(movieRecommendationQuery, rawResults=True, bindVars=bind)\n",
     "movieDataFrame = pd.DataFrame.from_dict(movieData)\n",
     "movieDataFrame.head(10)"

notebooks/arangoflix/similarMovie_TFIDF_ML_Inference.ipynb

@@ -158,40 +158,6 @@
     "print(\"Database: \" + con[\"dbName\"])\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "CyYMgGjZtayz"
-   },
-   "source": [
-    "##Configure connection to existing database (Path 2 or Path 3)\n",
-    "To configure the connection, you will need:\n",
-    "1. hostname, e.g., \"tutorials.arangodb.cloud\"\n",
-    "2. port, e.g., \"8529\"\n",
-    "2. userName, e.g., \"root\"\n",
-    "3. password, e.g., \"openSesame\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "C1_C6KbCuRPd"
-   },
-   "outputs": [],
-   "source": [
-    "hostname = \"tutorials.arangodb.cloud\"\n",
-    "port = \"8529\"\n",
-    "dbName = \"TUTvhtmpwfo287c373bktu7ka\"\n",
-    "username =\"TUTp9494mxptur3pyxtptu9dy\"\n",
-    "password =\"TUT6qoh8j20r9usl145v5b3m\"\n",
-    "# Initialize the ArangoDB client.\n",
-    "from arango import ArangoClient\n",
-    "client = ArangoClient(\"http://\"+hostname+\":\"+port)\n",
-    "## Connect to the database\n",
-    "db = client.db(hostname+\":\"+port, username=username, password=password)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {